This paper proposes a new method to trace the transmission loss in deregulated power system by applying Genetic Algorithm (GA) and Least Squares Support Vector Machine (LS-SVM). The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of LS-SVM and adopt a supervised learning approach to train the LS-SVM model. The well known proportional sharing method (PSM) is used to trace the loss at each transmission line which is then utilized as a teacher in the proposed hybrid technique called GA-SVM method. Based on load profile as inputs and PSM output for transmission loss allocation, the GA-SVM model is expected to learn which generators are responsible for transmission losses. In this paper, IEEE 14-bus system is used to show the effectiveness of the proposed method ...
TY - JOUR. T1 - Direct determination of leather dyes by visible reflectance spectroscopy using partial least-squares regression. AU - Blanco, M.. AU - Canals, T.. AU - Coello, J.. AU - Gené, J.. AU - Iturriaga, H.. AU - Maspoch, S.. PY - 2000/9/1. Y1 - 2000/9/1. N2 - A method for the simultaneous spectrophotometric determination of leather dyes based on their reflectance spectrum in the visible region and the use of partial least-squares regression (PLSR) is proposed. Calibration was done by using absorption, first- and second-derivative spectra for five sets of 29 different mixtures of Sellaset H dyes over the wavelength range 400-900nm. The proposed multivariate calibration method allowed us to develop a model adjusted to the dye concentrations in the mixtures used at the calibration stage. The method was validated with five test sets consisting of eight samples each.The proposed method allows the accurate, highly expeditious quantitation of various dyes without the need for a prior ...
BackgroundThe objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used.MethodsData consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content.ResultsIn the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for
Objective: One of the important life-threatening ailment is stroke across the world. The current paper was performed to classify the outcome of stroke..
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
In this reaction, the hydrogen atom that belongs to cellulose hydroxyl is replaced by an acetic group from acetic anhydride, yielding a carboxylic acid and an ester group in the wood, which is less polar to strong hydroxyl. This strategy renders wood more compatible with apolar polymers such as polyolefins.. Hence, the objectives of this study were to verify the effects of the proportion of acetic acid/anhydride on the acetylation of wood flour at varying times and temperatures and to examine hydroxyl and carbonyl groups by FTIR. The influence of these parameters was verified by experiments with a factorial design and partial least squares regression, both useful techniques for obtaining information concerning the effect of parameters in a given process.25-28 EXPERIMENTAL. Materials. Wood flour (100 mesh) from Pinus sp. trees, with a density of 0.25 g/cm³, was supplied by Pinhopó Ltda. Acetic acid and acetic anhydride (both from Biotec) were used in different ratios in the acetylation ...
TY - JOUR. T1 - Predictive performance of bayesian and nonlinear least-squares regression programs for lidocaine. AU - Destache, Christopher J.. AU - Hilleman, Daniel E.. AU - Mohiuddin, Syed M.. AU - Lang, Patricia T.. PY - 1992. Y1 - 1992. N2 - The predictive performance of two computer programs for lidocaine dosing were evaluated. Two-compartment Bayesian and nonlinear least-squares regression programs were used in two groups of patients (15 acute arrhythmia patients and 14 chronic arrhythmia patients). Lidocaine was given as a 1.5 mg/kg bolus and a 2.8 mg/min infusion for 48 h. A second bolus (0.5 mg/kg) was given 10 min after the first bolus over 2 min. Serum samples of the patients receiving lidocaine were drawn at 2, 15, 30 min and 1, 2, and 4 h and were used in forecasting the serum concentrations at 6, 8, 12, and 48 h. Predictive performance was assessed by mean error and mean-squared error. The results (mean ± 95% confidence intervals) demonstrated the Bayesian program predicted a ...
Methods and Results-In 86 stable patients with HF and EF ≥45% in the Karolinska Rennes (KaRen) biomarker substudy, biomarkers were quantified by a multiplex immunoassay. Orthogonal projection to latent structures by partial least square analysis was performed on 87 biomarkers and 240 clinical variables, ranking biomarkers associated with New York Heart Association (NYHA) Functional class and the composite outcome (all-cause mortality and HF hospitalization). Biomarkers significantly correlated with outcome were analyzed by multivariable Cox regression and correlations with echocardiographic measurements performed. The orthogonal partial least square outcome-predicting biomarker pattern was run against the Ingenuity Pathway Analysis (IPA) database, containing annotated data from the public domain. The orthogonal partial least square analyses identified 32 biomarkers correlated with NYHA class and 28 predicting outcomes. Among outcome-predicting biomarkers, growth/differentiation factor-15 was ...
In this paper, a new technique for predicting human lower limb periodic motions from multi-channel surface ElectroMyoGram (sEMG) was proposed on the
Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and nondestructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination
Partial least squares model based on low mass range (2.5-20 kDa) data from the supernatant fraction of three normal tissue (grey) and three tumour tissue sa
Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed
Vibroarthographic (VAG) signals emitted from the knee joint disorder provides an early diagnostic tool. The nonstationary and nonlinear nature of VAG signal makes an important aspect for feature extraction. In this work, we investigate VAG signals by proposing a wavelet based decomposition. The VAG signals are decomposed into sub-band signals of different frequencies. Nonlinear features such as recurrence quantification analysis (RQA), approximate entropy (ApEn) and sample entropy (SampEn) are extracted as features of VAG signal. A total of twenty-four features form a vector to characterize a VAG signal. Two feature selection (FS) techniques, apriori algorithm and genetic algorithm (GA) selects six and four features as the most significant features. Least square support vector machines (LS-SVM) and random forest are proposed as classifiers to evaluate the performance of FS techniques. Results indicate that the classification accuracy was more prominent with features selected from FS algorithms. ...
Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests). The features extracted from visible images were pixel-wise color and texture features, while region-wise BA (Biospeckle Activity) was calculated from biospeckle imaging. In addition, the calculation of BA using varied numbers of speckle patterns were compared. Finally, extracted features were implemented into classifiers of LS-SVM (Least Square Support Vector Machine) and BLR (Binary Logistic Regression), respectively. Results showed that color features performed
Lymph node status is not part of the staging system for cervical cancer, but provides important information for prognosis and treatment. We investigated whether lymph node status can be predicted with proteomic profiling. Serum samples of 60 cervical cancer patients (FIGO I/II) were obtained before primary treatment. Samples were run through a HPLC depletion column, eliminating the 14 most abundant proteins ubiquitously present in serum. Unbound fractions were concentrated with spin filters. Fractions were spotted onto CM10 and IMAC30 surfaces and analyzed with surface-enhanced laser desorption time of flight (SELDI-TOF) mass spectrometry (MS). Unsupervised peak detection and peak clustering was performed using MASDA software. Leave-one-out (LOO) validation for weighted Least Squares Support Vector Machines (LSSVM) was used for prediction of lymph node involvement. Other outcomes were histological type, lymphvascular space involvement (LVSI) and recurrent disease. LSSVM models were able to determine LN
View Notes - OLS_Assumptions08B from ESM 206 at University of California, Santa Barbara. Assumptions of Ordinary Least Squares Regression (Part 1) ESM 206 Jan 17, 2008 1 Assumptions of OLS
On Friday, May 26th well be releasing a new recorded webinar entitled Introduction to Ordinary Least Squares Regression in ArcGIS. The OLS tool in the Spatial Statistics Toolbox of ArcGIS helps us determine the variables that explain why an observed pattern is present. These explanatory variables can lead to the development of models that can predict the occurrence of these patterns in other places.. There are essentially three primary reasons to use regression analysis. First is to gain an understanding of a phenomenon and effect policy or make decisions about appropriate actions to take. The next is to create predictive models of a phenomenon that can be applied to other areas, and finally, to explore hypotheses. In this webinar youll learn the basic concepts behind the OLS tool in ArcGIS.. This recorded webinar will be released to all current members of our email list on Friday, May 26th. Use the Signup box at the top of this page to make sure you get access to this webinar.. Learn more ...
Monotone function, such as growth function and cumulative distribution function, is often a study of interest in statistical literature. In this dissertation, we propose a nonparametric least-squares method for estimating monotone functions induced from stochastic processes in which the starting time of the process is subject to interval censoring. We apply this method to estimate the mean function of tumor growth with the data from either animal experiments or tumor screening programs to investigate tumor progression. In this type of application, the tumor onset time is observed within an interval. The proposed method can also be used to estimate the cumulative distribution function of the elapsed time between two related events in human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) studies, such as HIV transmission time between two partners and AIDS incubation time from HIV infection to AIDS onset. In these applications, both the initial event and the subsequent event are only
Anderson, G. J., and Mizon, G. E. (1983). Parameter Constancy Tests: Old and New, Discussion Paper 8325, Economics Department, University of Southampton.. Andreou, E., and Ghysels, E. (2002). Detecting Multiple Breaks in Financial Market Volatility Dynamics, Journal of Applied Econometrics, 17: 579-600.. Andrews, D. W. K. (1993). Tests for Parameter Instability and Structural Change with Unknown Change Point, Econometrica, 61: 821-856.. Andrews, D. W. K. (2003). End-of-Sample Instability Tests, Econometrica, 71: 1661-1694.. Andrews, D. W. K., and Fair, R. C. (1988). Inference in Nonlinear Econometric Models with Structural Change, The Review of Economic Studies, 55: 615-639.. Bai, J. (1994). Least Squares Estimation of a Shift in Linear Processes, Journal of Time Series Analysis, 15: 453-472.. Bai, J. (1995). Least Absolute Deviation Estimation of a Shift, Econometric Theory, 11: 403-436.. Bai, J. (1997). Estimation of a Change Point in Multiple Regression Models, Review of ...
It is usually more convenient to base programs for nonlinear regression on matrix algebra. This is the approach taken in higher level mathematical programming software such as that provided by Matlab and Mathcad (sources for the Matlab and Mathcad software are listed at the end of this chapter). The principles are exactly the same as in the algebraic approach discussed above, but matrix methods facilitate organization and manipulation of the data.. In matrix notation, the straight-line model can be expressed as [3, 5]. where Y is a vector containing the n values of y; (meas), X is an n X 2 sample matrix, e is a vector containing the observed residuals, and b is the vector containing values of the slope and intercept. For an example with n = 3, eq. (2.17) can be represented as in Box 2.1.. ...
Currently, many studies have focused on the magnitude of coherence with less emphasis on the time delay, or have mostly used only one method to establish the temporal relationship between the sensorimotor cortex and the peripheral muscles. Here, the time delays using inverse Fast Fourier transformation (IFFT), least squares regression analysis (LSR), weighted least squares regression analysis (WLSR), maximum coherence (MAX-COH) and mean of significant coherences (MEAN-COH) methods in the same subjects are compared to clarify the best method(s) for electroencephalography (EEG)- electromyography (EMG) temporal analysis. EEG activity and surface EMG activity from the first dorsal interosseous (FDI) muscle of the right hand were recorded in eight normal subjects during a weak contraction task. The current source density (CSD) reference method was estimated and used in the phase and temporal analysis. For the EEG and EMG time delay in the same subjects, MAX-COH, MEAN-COH and LSR methods are found to ...
Preface xiii. Acknowledgments xv. Abbreviations xvii. 1 Identification 1. 1.1 Introduction 1. 1.2 Illustration of Some Important Aspects of System Identification 2. Exercise 1 .a (Least squares estimation of the value of a resistor) 2. Exercise 1 .b (Analysis of the standard deviation) 3. Exercise 2 (Study of the asymptotic distribution of an estimate) 5. Exercise 3 (Impact of noise on the regressor (input) measurements) 6. Exercise 4 (Importance of the choice of the independent variable or input) 7. Exercise 5.a (combining measurements with a varying SNR: Weighted least squares estimation) 8. Exercise 5.b (Weighted least squares estimation: A study of the variance) 9. Exercise 6 (Least squares estimation of models that are linear in the parameters) 11. Exercise 7 (Characterizing a 2-dimensional parameter estimate) 12. 1.3 Maximum Likelihood Estimation for Gaussian and Laplace Distributed Noise 14. Exercise 8 (Dependence of the optimal cost function on the distribution of the disturbing noise) ...
A series of images are acquired by optical intrinsic signal imaging (OISI) at 550 nm during cortical spreading depression (CSD) in rats. Temporal clustering analysis (TCA), which is an exploratory data-driven technique that has been proposed for the analysis of fMRI data and laser speckle contrast images, is applied to tract the extreme response during CSD. The minimum of optical intrinsic signals (OIS) during CSD in each pixel, corresponding to the maximum change of regional cerebral blood volume (CBV), is determined by TCA. Interestingly, the spatial pattern of the maximum activation shows the ongoing expanding circle. In order to describe the circular pattern quantitatively, we present the least square estimation (LSE) to detect the three parameters of the expanding circles (radius R, center coordinates (a, b)) at each time point (i.e. in each frame). The evaluated mean centers of the circles (1.50±0.06 mm, 2.62±0.03 mm) were tightly correlated with the pinprick site (1.4±0.2 mm, 2.5±0.2 ...
The partial least squares method has many advantages in multivariable linear regression, but it does not include the function of feature selection. This method cannot screen for the best feature subset (referred to in this study as the “Gold Standard”) or optimize the model, although contrarily using the L1 norm can achieve the sparse representation of parameters, leading to feature selection. In this study, a feature selection method based on partial least squares is proposed. In the new method, exploiting partial least squares allows extraction of the latent variables required for performing multivariable linear regression, and this method applies the L1 regular term constraint to the sum of the absolute values of the regression coefficients. This technique is then combined with the coordinate descent method to perform multiple iterations to select a better feature subset. Analyzing traditional Chinese medicine data and University of California, Irvine (UCI), datasets with the model, the
A method for improving the results of radio location systems that incorporate weighted least squares optimization generalizes the weighted least squares method by using maximum a posteriori (MAP) probability metrics to incorporate characteristics of the specific positioning problem (e.g., UTDOA). Weighted least squares methods are typically used by TDOA and related location systems including TDOA/ AOA and TDOA/GPS hybrid systems. The incorporated characteristics include empirical information about TDOA errors and the probability distribution of the mobile position relative to other network elements. A technique is provided for modeling the TDOA error distribution and the a priori mobile position. A method for computing a MAP decision metric is provided using the new probability distribution models. Testing with field data shows that this method yields significant improvement over existing weighted least squares methods.
Textbook solution for Principles of Instrumental Analysis 7th Edition Douglas A. Skoog Chapter 10 Problem 10.11QAP. We have step-by-step solutions for your textbooks written by Bartleby experts!
During conversion of bamboo into biofuels and chemicals, it is necessary to efficiently predict the chemical composition and digestibility of biomass. However, traditional methods for determination of lignocellulosic biomass composition are expensive and time consuming. In this work, a novel and fast method for quantitative and qualitative analysis of chemical composition and enzymatic digestibilities of juvenile bamboo and mature bamboo fractions (bamboo green, bamboo timber, bamboo yellow, bamboo node, and bamboo branch) using visible-near infrared spectra was evaluated. The developed partial least squares models yielded coefficients of determination in calibration of 0.88, 0.94, and 0.96, for cellulose, xylan, and lignin of bamboo fractions in raw spectra, respectively. After visible-near infrared spectra being pretreated, the corresponding coefficients of determination in calibration yielded by the developed partial least squares models are 0.994, 0.990, and 0.996, respectively. The score plots of
Hinterstoisser B., Schwanninger M., Rodrigues JC., Gierlinger N.: Determination of lignin content in Norway spruce wood by Fourier transformed near infrared spectroscopy and partial least squares regression analysis. Part 2: Development and evaluation of the final model, J Near Infrared Spec., 19(5), 2011, 331- ...
Ill add a real response here, following the comment I just left. As @GeoffOxberry suggests, you might be able to use active subspaces to, in essence, preprocess your objective function and eliminate (linear combinations of) variables. Try the following first. Randomly sample your variables according to some density. One reasonable choice would be to sample according to a Gaussian that is relatively large in a region that may contain the optimum. (Make sure $f$ is well-defined for each sample!) Call these samples $\mathbf{x}_j$ with $j=1,\dots,N$. With 20 variables, Id say try 40-100 samples, if you can. For each $\mathbf{x}_j$, compute the gradient, $$\nabla f_j \;=\; \nabla f(\mathbf{x}_j) \;\in\; \mathbb{R}^{20}.$$ Put all these gradient vectors into a matrix $$\mathbf{G} \;=\; \frac{1}{\sqrt{N}}\begin{bmatrix} \nabla f_1 & \cdots & \nabla f_N \end{bmatrix} \;\in\; \mathbb{R}^{20\times N},$$ and compute its SVD, $\mathbf{G}=\mathbf{U}\Sigma\mathbf{V}^T$. Look for gaps in the singular ...
|p|In the present paper QSAR modeling using electrotopological state atom (E-state) parameters has been attempted to determine the antiradical and the antioxidant activities of flavonoids in two model systems reported by Burda et al. (2001). The antiradical property of a methanolic solution of 1, 1-diphenyl-2-picrylhydrazyl (DPPH) and the antioxidant activity of flavonoids in a β-carotenelinoleic acid were the two model systems studied. Different statistical tools used in this communication are stepwise regression analysis, multiple linear regressions with factor analysis as the preprocessing step for variable selection (FA-MLR) and partial least squares analysis (PLS). In both the activities the best equation is obtained from stepwise regression analysis, considering, both equation statistics and predictive ability (antiradical activity: R 2 = 0.927, Q2 = 0.871 and antioxidant activity: R 2 = 0.901, Q2 = 0.841). |inline-formula||alternatives| [...] |/alternatives||/inline
Gene Expression Data Classification with Revised Kernel Partial Least Squares Algorithm - One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N | M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel partial least squares (KPLS) and classification with logistic regression (discrimination) and other standard machine learning methods. KPLS is a generalization and nonlinear version of partial least squares (PLS). The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in
Genedata Analyst™ is the premier software solution for the integration and interpretation of experimental data in life science R&D. It puts rigorous statistical algorithms, interactive data analysis tools, and intuitive visualization into the hands of researchers and biostatisticians alike. Built on a scalable client-server architecture with a rich set of APIs, Genedata Analyst provides a centrally managed, secure, and scalable data mining platform that can be easily integrated into existing research IT environments. Advanced interactive data mining and visualizations are complemented by statistical applications including t-Test, ANOVA, linear models, Principal Components Analysis (PCA), Partial Least Square analysis (PLS), and many more. ...
1] Exploring Polymeric Nano-Particles as Targeted Pulmonary Delivery of Rifampicin, Ethambutol and Ofloxacin against Inh-Resistant Tuberculosis. J Lung Pulm Respir Res 4(1): 00116. DOI: 10.15406/jlprr.2017.04.00116 ISSN 2376-0060. [2] Preparation, Optimization and in Vitro Characterization of Cisplatin Loaded Novel Polymeric Micelles for Treatment of Lung Cancer. IJRSI International journal of research in scientific innovation 4 (1) : 431-441. ISSN 2321-2705. [3] Research paper entitled Identification of key variables affecting drug release from lipid matrix in hydroalcoholic dissolution medium containing hydroxymethyl propyl cellulose in Indian drugs 53(11) 2016 [4] Asha Patel, Mukesh Gohel, Tejal Soni Partial Least Square Analysis and Mixture Design for the study of the influence of composition variables on Nanoemulsions as drug carriers. Research Journal of Pharmacy and technology, 7(12): December, 2014, 1446-1452.. [5] Development of plant extract loaded Nanoemulsion for the treatment of ...
Protein-based biopharmaceuticals are becoming increasingly widely used as therapeutic agents, and the characterization of these biopharmaceuticals poses a significant analytical challenge. In particular, monitoring posttranslational modifications (PTMs), such as glycosylation, is an important aspect of this characterization because these glycans can strongly affect the stability, immunogenicity, and pharmacolcinetics of these biotherapeutic drugs. Raman spectroscopy is a powerful tool, with many emerging applications in the bioprocessing arena. Although the technique has a relatively rich history in protein science, only recently has Raman spectroscopy been investigated for assessing posttranslational modifications, including phosphorylation, acetylation, trimethylation, and ubiquitination. In this investigation, we develop for the first time Raman spectroscopy combined with multivariate data analyses, including principal components analysis and partial least-squares regression, for the ...
from http://www.cnblogs.com/tychyg/p/4868626.html Basis(基础)： MSE(Mean Square Error 均方误差)，LMS(LeastMean Square 最小均方)，LSM(Least Square Methods 最小二乘法)，MLE(MaximumLikelihood Estimati
A spectrophotometric method for selective complexation reaction and simultaneous determination of mycophenolate mofetil (MPM), and mycophenolic acid (MPA) using three multivariate chemometric methods, i.e. partial least squares regression, principal component regression and principal component artificial neural networks, is proposed. The method is based on the complexation reaction of MPM and MPA with Fe(III) ion in the solution. A nonionic surfactant, Triton X-100, was used for dissolving the complexes and intensifying the signals. The linear determination ranges for the determination of MPA and MPM were 5.0-215.0 mg l-1, and 10.0-1000.0 mg l-1, respectively. The detection limit for MPA and MPM was obtained as 0.3 mg l-1 and 1.1 mg l-1, respectively. Satisfactory results were obtained by the combination of spectrophotometric method and chemometrics techniques. The method was successfully applied to the simultaneous determination of MPM and MPA in serum sample and the results were comparable with HPLC
Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Caos method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results ...
Here you can post any question you have that is not already covered elsewhere on the site or respond to the posts of other members if you know the answer to their question. When posting a question, please include the text of the problem you want answered. I will try to respond quickly, and when necessary, I will even include a video response. Finally, your question may have been asked already, so be sure to check the categories below before posting.. Recently asked questions for category: Least Squares Regression ...
p,The K-S tests were build upon the null hypothesis, F_n(x)=F(x), not on \hat{\theta}=\theta_o (parameter). The confidence band is laid upon the empirical distribution, if I put this in a simple word, a confidence band on the estimated p-value. I didnt see that they included any parameter estimation and its confidence band. ,/p, ,p,The χ^2 method provides a best fit based on least square methods. If the errors of the response variable are normally distributed, then this least square method provides an equal solution to the maximum likelihood estimator. Because of Wald (1949) and other series of works, we know that this ML estimator is consistent and asymptotically normal, so that the χ^2 number +1 could lead a 68% confidence interval (one parameter). This interval is only valid when the ML estimator is consistent. Protossov et.al. (2001) showed some of the regularity conditions on the model to reach this consistent estimator. Im not sure all astronomical models satisfy these conditions to ...
Quantile regression have its advantage properties comparing to the OLS model regression which are full measurement of the effects of a covariate on response, robustness and Equivariance property. In this paper, I use a survey data in Belgium and apply a linear model to see the advantage properites of quantile regression. And I use a quantile regression model with the raw data to analyze the different cost of family on different numbers of children and apply a Wald test. The result shows that for most of the family types and living standard, from the lower quantile to the upper quantile the family cost on children increases along with the increasing number of children and the cost of each child is the same. And we found a common behavior that the cost of the second child is significantly more than the cost of the first child for a nonworking type of family and all living standard families, at the upper quantile (from 0.75 quantile to 0.9 quantile) of the conditional distribution.. ...
Anyway, in either above cases the line is estimated by using the so-called least squares method: the best line is the one for which the sum of squares of differences between the points and line itself is minimum.. In first model, the level for which one can obtain the minimum of sum of squares of distance of points from line is at the average of weight values $$(\overline{y}$$). The dashed green lines represent the distances of some observed points from the best (estimated) line.. In second model, the result of application of least-squares method is a few more complex, but can be obtained in closed form, as:. ...
Anyway, in either above cases the line is estimated by using the so-called least squares method: the best line is the one for which the sum of squares of differences between the points and line itself is minimum.. In first model, the level for which one can obtain the minimum of sum of squares of distance of points from line is at the average of weight values $$(\overline{y}$$). The dashed green lines represent the distances of some observed points from the best (estimated) line.. In second model, the result of application of least-squares method is a few more complex, but can be obtained in closed form, as:. ...
We provide a generalization of the Anderson-Rubin (AR) procedure for inference on parameters which represent the dependence between possibly endogenous explanatory variables and disturbances in a linear structural equation (endogeneity parameters). We focus on second-order dependence and stress the distinction between regression and covariance endogeneity parameters. Such parameters have intrinsic interest (because they measure the effect of common factors which induce simultaneity) and play a central role in selecting an estimation method (because they determine simultaneity biases associated with least-squares methods). We observe that endogeneity parameters may not identifiable and we give the relevant identification conditions. We develop identification-robust finite-sample tests for joint hypotheses involving structural and regression endogeneity parameters, as well as marginal hypotheses on regression endogeneity parameters. For Gaussian errors, we provide tests and confidence sets ...
This paper presents a study into the potential of visible spectroscopy with chemometrics as an approach to dating blue ball tip inks on paper documents. Analysis of six inks left under various conditions found that the majority of those kept in the dark could still be matched to the source pen after 32 months of ageing. Conversely, the majority of those exposed to light exhibited rapid spectral changes that continued throughout the 32 month period. Partial least squares regression (PLSR) was used to generate dating models for inks aged with exposure to light. Evaluation using an external test set found absolute dating to be challenging for these ink deposits within the first 2-6 months of ageing. However, predictive accuracy was found to improve for long-term ageing, with two-year old samples yielding age estimates with a maximum error of 6 months. This rapid, non-destructive methodology could assist document examiners in the relative ageing or approximate age determination of questioned ...
The main objective of this paper is to apply genetic programming (GP) with an orthogonal least squares (OLS) algorithm to derive a predictive model for the compressive strength of carbon fiber-reinforced plastic (CFRP) confined concrete cylinders. The GP/OLS model was developed based on experimental results obtained from the literature. Traditional GP-based and least squares regression analyses were performed using the same variables and data sets to benchmark the GP/OLS model. A subsequent parametric analysis was carried out and the trends of the results were confirmed via previous laboratory studies. The results indicate that the proposed formula can predict the ultimate compressive strength of concrete cylinders with an acceptable level of accuracy. The GP/OLS results are more accurate than those obtained using GP, regression, or several CFRP confinement models found in the literature. The GP/OLS-based formula is simple and straightforward, and provides a valuable tool for analysis. ...
Harold Averkamp (CPA, MBA) has worked as a university accounting instructor, accountant, and consultant for more than 25 years. He is the sole author of all the materials on AccountingCoach.com. ...
Activity: Tootie Fruities. 1.Each person grabs one hand full of Tootie Fruities and does a quantitative analysis of the that event (how much did you grab?). Discuss an exact procedure on how to grab the cereal. 2.Make a histogram of the data and discuss the shape center and spread of the data.
OBJECTIVES: To identify prognostic surrogate markers for change in cognitive states of HIV-infected patients. DESIGN: Longitudinal cerebrospinal fluid (CSF) samples were collected from 98 HIV-infected patients identified by temporal change in cognitive states classified as normal, stably impaired, improving and worsening. METHODS: The metabolic composition of CSF was analysed using H nuclear magnetic resonance (H NMR) spectroscopy that focused on energy metabolites. Metabolic biomarkers for cognitive states were identified using multivariate partial least squares regression modelling of the acquired spectra, combined with nonparametric analyses of metabolites with clinical features. RESULTS: Multivariate modelling and cross-validated recursive partitioning identified several energy metabolites that, when combined with clinical variables, classified patients based on change in neurocognitive states. Prognostic identification for worsening was achieved with four features that included no change in a
Cellular behavior in response to stimulatory cues is governed by information encoded within a complex intracellular signaling network. An understanding of how phenotype is determined requires the distributed characterization of signaling processes (e.g., phosphorylation states and kinase activities) in parallel with measures of resulting cell function. We previously applied quantitative mass spectrometry methods to characterize the dynamics of tyrosine phosphorylation in human mammary epithelial cells with varying human epidermal growth factor receptor 2 (HER2) expression levels after treatment with epidermal growth factor (EGF) or heregulin (HRG). We sought to identify potential mechanisms by which changes in tyrosine phosphorylation govern changes in cell migration or proliferation, two behaviors that we measured in the same cell system. Here, we describe the use of a computational linear mapping technique, partial least squares regression (PLSR), to detail and characterize signaling mechanisms
Nowadays, a large number of chiral analyses are needed, simple and rapid methods for the determination of the enantiomeric composition in pharmaceutical products should be developed. The fluoxetine belongs to the most prescribed antidepressant chiral drugs and its enantiomers have a different duration of serotonin inhibition. This study presents cheap and fast alternative to traditional chiral techniques for the determination of the fluoxetine enantiomeric composition by the combination of UV/VIS spectrometry and multivariate calibration. The chiral recognition of the fluoxetine was based on the creating of the diastereomeric complexes with α- and β- cyclodextrin. Multivariate calibration methods, including principal component regression (PCR) and partial least square method (PLS), were used for spectral data evaluation. Small differences in results between eachcyclodextrin and both calibration models were obtained by multivariate calibration. PLS model for determination of the enantiomeric ...
Raman spectroscopy has numerous applications in the field of biology. One such application is the simultaneously measurement of the concentration of multiple biochemical components in low volume aqueous mixtures, for example, a single drop of blood serum. Over twenty years ago, it was shown for the first time that it was possible to estimate the concentration of glucose, urea, and lactic acid in mixture by combining Raman Spectroscopy with Partial Least Squares Regression analysis. This was followed by numerous contributions in the literature designed to increase the number of components and reduce the limits of concentration that could be simultaneously measured using Raman spectroscopy, by developing various optical architectures to maximise the signal to noise ratio. The aim of this paper is to demonstrate the potential of a confocal Raman microscopy system for multicomponent analysis for the case of physiologically relevant mixtures of glucose, urea, and lactic acid.. ...
Antitubercular activity of 5-nitrofuran-2-yl Derivatives series were subjected to Quantitative Structure Activity Relationship (QSAR) Analysis with an effort to derive and understand a correlation between the biological activity as response variable and different molecular descriptors as independent variables. QSAR models are built using 40 molecular descriptor dataset. Different statistical regression expressions were got using Partial Least Squares (PLS),Multiple Linear Regression (MLR) and Principal Component Regression (PCR) techniques. The among these technique, Partial Least Square Regression (PLS) technique has shown very promising result as compared to MLR technique A QSAR model was build by a training set of 30 molecules with correlation coefficient ($r^2$) of 0.8484, significant cross validated correlation coefficient ($q^2$) is 0.0939, F test is 48.5187, ($r^2$) for external test set (pred$_r^2$) is -0.5604, coefficient of correlation of predicted data set (pred$_r^2se$) is 0.7252 and ...
This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial Least-Squares Regression (PLSR) was applied as a multivariate regression algorithm. The univariate models were used to model the number of available bikes at each station. PLSR was applied to reduce the number of required prediction models and reflect the spatial correlation between stations in the network. Results clearly show that univariate models have lower error predictions than the multivariate model. However, the multivariate model results are reasonable for networks with a relatively large number of spatially correlated stations. Results also show that station neighbors and the prediction horizon time are significant predictors. The most effective prediction horizon time that produced the least prediction error was 15 minutes.. Huthaifa I. Ashqar, ...
Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450-1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression ...
Serum and urine samples were collected from 27 patients with dry and 75 with wet macular degeneration. Serum samples were centrifuged to remove cells, and 0.5ml aliquots stored at -80 degree C. After thawing, serum was filtered through 3kD MW cutoff filter to remove proteins. The filtrate was made with 10% in D2O, 100mM phosphate 0.5mM TMSP and pH 7.00.One-dimensional 1H spectra were acquired using a standard spin-echo pulse sequence on a Bruker DRX 600MHz NMR spectrometer equipped with a 1.7mm cryoprobe. 2D JRes spectra were also acquired to aid metabolite identification. Spectra were be segmented into 0.005-ppm (2.5 Hz) chemical shift bins between 0.2 and 10.0 ppm, and the spectral area within each bin integrated. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of the processed data was conducted using PLS Toolbox (Eigenvector Research) within MATLAB ...
Abstract: This study monitored structural shifts of gut microbiota of rats developing precancerous mucosal lesions induced by carcinogen 1,2-dimethyl hydrazine (DMH) treatment using PCR-denaturing gradient gel electrophoresis (DGGE) and 454 pyrosequencing on the 16S rRNA gene V3 region. Partial least square discriminant analysis of DGGE fingerprints showed that the gut microbiota structure of treated animals was similar to that of the controls 1 and 3 weeks after DMH treatments, but significantly different 7 weeks after DMH treatments, when a large number of aberrant crypt foci (ACF) developed in their colons. Martens uncertainty test, followed by anova test ( ...
Skeletal trauma analysis of motor vehicle collisions has the potential to support or contradict reported collision circumstances. This project analyzed the skeletal injuries that pedestrians sustain in fatal collisions according to vehicle types (car, truck, SUV, van, bus, semi, etc.). Data were collected from reports and databases related to cases that occurred in King County, Washington. The pelvis and lower extremities of the body were analyzed for the frequency of skeletal fractures, grouped by pelvis, femora, patellae, tibiae, and fibulae skeletal groups. A Kruskal-Wallis test showed an overall no significant difference (P|0.05) in fracture quantity in skeletal regions between different vehicle groups. A multiple pairwise comparison using Dunns procedure also found no significant differences between vehicle type groups. A Partial Least Squares Discriminant Analysis showed an overall success rate of 37.29% when classifying injury profiles to vehicle type. The findings of this project can be applied
Recent application of Fourier transform near infra-red spectroscopy (FT-NIRS) to predict age in fish otoliths has gained attention among fisheries managers as a potential alternative to costly production ageing of managed species. We assessed the age prediction capability of FT-NIRS scans in whole otoliths from red snapper, Lutjanus campechanus, collected from the US Gulf of Mexico and US Atlantic Ocean (South Atlantic). Otoliths were scanned with an FT-NIR spectrometer and resulting spectral signatures were regressed with traditionally estimated ages via partial least squares regression to produce calibration models, which were validated for predictive capability against test sets of otoliths. Calibration models successfully predicted age with R2 ranging 0.94-0.95, mean squared error ≤1.8 years, and bias ...
In order to predict omega-6 and omega-3 fatty acids in the diet of humans, seventy-three pork back fat adipose tissue samples were measured with Raman spectroscopy directly on adipose tissue and on melted fat. Melted fat samples were, in addition, measured with Fourier transform infrared (FT-IR) spectroscopy. Gas chromatography analyses were conducted as the reference analysis. Partial least squares regression (PLSR) was used to calibrate and validate all models predicting omega-3 and omega-6 fatty acids contents from spectra. Omega-6 fatty acids in melted fat measured with FT-IR was predicted with a correlation coefficient (,i,R,/i,) of 0.93 and a root mean square error of cross-validation (RMSECV) of 1.61% of the total amount of fatty acids. Raman spectra measured on melted fat gave a prediction of omega-6 fatty acids with ,i,R,/i, = 0.97, and RMSECV = 0.99% of total amount of fatty acids. Omega-6 fatty acids were predicted with ,i,R,/i, = 0.94, and RMSECV = 1.50% of the total amount of fatty ...
In practice, not only in EEG data analysis, often only the well-known PCA (principal component analysis, see https://en.wikipedia.org/wiki/Principal_component_analysis) and for classification in the best case an LDA (linear discriminant analysis, see https://en.wikipedia.org/wiki/Linear_discriminant_analysis) is used together with a support vector machine, see https://en.wikipedia.org/wiki/Support_vector_machine, thus reducing an often non-linear problem to a linear problem.. The non-linearity of the problem is often not adequately addressed by research groups, e.g. they often limit themselves to spectral analyses. However, there is usually much more information to be gained from the data, and this is already the case when using Partial least Square Regression (PLSR) and its further developments instead of linear regression or an LDA-based classification, see https://en.wikipedia.org/wiki/Partial_least_squares_regression.. The same applies to the support vector machine (SVM), which is robust and ...
Moisture content in commercially available milk powder was investigated using near infrared (NIR) diffuse reflectance spectroscopy with an Indian low-cost dispersive NIR spectrophotometer. Different packets of milk powder of the same batch were procured from the market. Forty-five samples with moisture range 4-10% were prepared in the laboratory. Spectra of the samples were collected in the wavelength region 800-2500 nm. Moisture values of all the samples were simultaneously determined by Karl Fischer (KF) titration. These KF values were used as reference for developing calibration model using partial least squares regression (PLSR) method. The calibration and validation statistics are ...
Two multivariate calibration methods are compared for the simultaneous chromatographic determination and separation of Sulfamethoxazole (SMX) and Phthalazine (PHZ) by High Performance Liquid Chromatography (HPLC). Multivariate calibration techniques such as Classical Least Squares (CLS) and Inverse Least Squares (ILS) were introduced into HPLC to determine the quantification by using UV detector at 235, 250, 260 and 270 nm. Sixteen binary mixtures of SMX and PHZ as calibration set and eight binary mixtures as prediction set were used. Results show that, Relative Errors of Prediction (REP) of CLS and ILS for SMX and PHZ were 0.17%, 0.63% and o.15%, 0.56%, respectively.
Catchments impacted by wildfire typically experience elevated rates of post-fire erosion and formation and deposition of pyrogenic carbon (PyC). To better understand the role of erosion in post-fire soil carbon dynamics, we determined distribution of soil organic carbon in different chemical fractions before and after the Gondola fire in South Lake Tahoe, CA. We analyzed soil samples from eroding and depositional landform positions in control and burned plots pre- and post-wildfire (in 2002, 2003, and 10-years post-fire in 2013). We determined elemental concentrations, stable isotope compositions, and biochemical composition of organic matter (OM) using mid-infrared (MIR) spectroscopy for all of the samples. A subset of samples was analyzed by 13C cross polarization magic angle spinning nuclear magnetic resonance spectroscopy (CPMAS 13C-NMR). We combined the MIR and CPMAS 13C-NMR data in the Soil Carbon Research Program partial least squares regression model to predict distribution of soil carbon into
Simkin, AJ., López-Calcagno, PE. and Raines, CA., (2019). Feeding the world: improving photosynthetic efficiency for sustainable crop production. Journal of Experimental Botany. 70 (4), 1119-1140 Faralli, M., Cockram, J., Ober, E., Wall, S., Galle, A., Van Rie, J., Raines, C. and Lawson, T., (2019). Genotypic, Developmental and Environmental Effects on the Rapidity of gs in Wheat: Impacts on Carbon Gain and Water-Use Efficiency. Frontiers in Plant Science. 10, 492- Meacham-Hensold, K., Montes, CM., Wu, J., Guan, K., Fu, P., Ainsworth, EA., Pederson, T., Moore, CE., Brown, KL., Raines, C. and Bernacchi, CJ., (2019). High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity. Remote Sensing of Environment. 231, 111176-111176 Ermakova, M., Lopez-Calcagno, PE., Raines, CA., Furbank, RT. and von Caemmerer, S., (2019). Overexpression of the Rieske FeS protein of the Cytochrome b 6 f complex ...
|p style=text-indent:20px;|In classical regression analysis, the ordinary least-squares estimation is the best strategy when the essential assumptions such as normality and independency to the error terms as well as ignorable multicollinearity in the covariates are met. However, if one of these assumptions is violated, then the results may be misleading. Especially, outliers violate the assumption of normally distributed residuals in the least-squares regression. In this situation, robust estimators are widely used because of their lack of sensitivity to outlying data points. Multicollinearity is another common problem in multiple regression models with inappropriate effects on the least-squares estimators. So, it is of great importance to use the estimation methods provided to tackle the mentioned problems. As known, robust regressions are among the popular methods for analyzing the data that are contaminated with outliers. In this guideline, here we suggest two mixed-integer nonlinear optimization
Paper for Presentation at the 64th Annual Meeting of the American Society of Animal Science. The data consisted of 204,558 complete Holstein lactations in 2,100 herds compiled by the New York Dairy Records Processing Laboratory from 1959 to 1969. All lactation records had to have a 9th or 10th test day present to be regarded as complete. The generalized least squares analysis used a model including effects due to the mean, herd-year and season-age-stage of freshening which were assumed to be fixed. The error term included the random effects due to the cow and residual. Generalized least squares estimates for specified stages of lactation for milk and fat were found for lactations 1, 2, 3 and ≥ 4 which were grouped by age of freshening, two age groups for the first three lactations and one for ≥ four lactations. Extension factors will be presented which differ from current U.S.D.A. factors. The new factors emphasize the need for considering lactation number, season and age at freshening when
In this book chapter, Nancy Mead describes issues in developing security requirements, useful methods, including details about the SQUARE method.
Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes.
Introduction: Low molecular weight heparins (LMWHs) are used worldwide for the treatment and prophylaxis of thromboembolic disorders. Routine laboratory tests are not required due to the predictable pharmacokinetics of LMWHs, with the exception of pregnant patients, children, patients with renal failure, morbid obesity, or advanced age. Anti-Factor Xa (anti-FXa) plasma levels are most often employed in the assessment and guidance of accurate dosing in these patient cohorts. Materials and methods: A LMWH calibration curve was generated using citrated human pooled plasma spiked with pharmacologically relevant concentrations (0-1 U/ml) of two low molecular weight heparins; enoxaparin and tinzaparin. Least squares analysis determined the best curve fit for this set of data which returned low sum of squares (SS) values for the log linear fit with an R2 value of 0.995. Patient sample concentrations for the fluorogenic anti- FXa assay were determined using the log linear regression equation and correlated with
Topic: Supervisor Communication Authors, Title and Publication Mayfield, J., & Mayfield, M. (2012). The relationship between leader motivating language and self-efficacy: A partial least squares model analysis. Journal of Business Communication, 49(4), 357-376. Summary This study examined the nature and processes of the relationships between leader motivating language and its effects on employee self-efficacy and […]. ...
Downloadable! In the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the models forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil
TY - JOUR. T1 - Renal cell carcinoma survival and body mass index. T2 - a dose-response meta-analysis reveals another potential paradox within a paradox. AU - Bagheri, M. AU - Speakman, J R. AU - Shemirani, F. AU - Djafarian, K. N1 - The study was supported by Tehran University of Medical Sciences. We gratefully thank Dr Peter Lee for providing us with technical assistance.. PY - 2016/12. Y1 - 2016/12. N2 - BACKGROUND: In healthy subjects increasing body mass index (BMI) leads to greater mortality from a range of causes. Following onset of specific diseases, however, the reverse is often found: called the obesity paradox. But we recently observed the phenomenon called the paradox within the paradox for stroke patients.OBJECTIVE: The objective of our study was to examine the effect of each unit increase in BMI on renal cancer-specific survival (CSS), cancer-specific mortality, overall survival (OS) and overall mortality.DESIGN: Random-effects generalized least squares models for trend ...
Miscarriage and induced abortion are life events that can potentially cause mental distress. The objective of this study was to determine whether there are differences in the patterns of normalization of mental health scores after these two pregnancy termination events. Forty women who experienced miscarriages and 80 women who underwent abortions at the main hospital of Buskerud County in Norway were interviewed. All subjects completed the following questionnaires 10 days (T1), six months (T2), two years (T3) and five years (T4) after the pregnancy termination: Impact of Event Scale (IES), Quality of Life, Hospital Anxiety and Depression Scale (HADS), and another addressing their feelings about the pregnancy termination. Differential changes in mean scores were determined by analysis of covariance (ANCOVA) and inter-group differences were assessed by ordinary least squares methods. Women who had experienced a miscarriage had more mental distress at 10 days and six months after the pregnancy termination
In recent years, many algorithms based on kernel principal component analysis (KPCA) have been proposed including kernel principal component regression (KPCR). KPCR can be viewed as a non-linearization of principal component regression (PCR) which uses the ordinary least squares (OLS) for estimating its regression coefficients. We use PCR to dispose the negative effects of multicollinearity in regression models. However, it is well known that the main disadvantage of OLS is its sensitiveness to the presence of outliers. Therefore, KPCR can be inappropriate to be used for data set containing outliers. In this paper, we propose a novel nonlinear robust technique using hybridization of KPCA and R-estimators. The proposed technique is compared to KPCR and gives better results than KPCR.
Abstract: This review article considers some of the most common methods used in astronomy for regressing one quantity against another in order to estimate the model parameters or to predict an observationally expensive quantity using trends between object values. These methods have to tackle some of the awkward features prevalent in astronomical data, namely heteroscedastic (point-dependent) errors, intrinsic scatter, non-ignorable data collection and selection effects, data structure and non-uniform population (often called Malmquist bias), non-Gaussian data, outliers and mixtures of regressions. We outline how least square fits, weighted least squares methods, Maximum Likelihood, survival analysis, and Bayesian methods have been applied in the astrophysics literature when one or more of these features is present. In particular we concentrate on errors-in-variables regression and we advocate Bayesian techniques ...
Downloadable (with restrictions)! The objective of this article is to develop a computationally efficient estimator of the regression function subject to various shape constraints. In particular, nonparametric estimators of monotone and/or convex (concave) regression functions are obtained by using a nested sequence of Bernstein polynomials. One of the key distinguishing features of the proposed estimator is that a given shape constraint (e.g., monotonicity and/or convexity) is maintained for any finite sample size and satisfied over the entire support of the predictor space. Moreover, it is shown that the Bernstein polynomial based regression estimator can be obtained as a solution of a constrained least squares method and hence the estimator can be computed efficiently using a quadratic programming algorithm. Finally, the asymptotic properties (e.g., strong uniform consistency) of the estimator are established under very mild conditions, and finite sample properties are explored using several
Discovering Partial Least Squares with JMP by Cox Ian (ISBN: 978-1-61290-822-9); Published by SAS Institutein Oct 2013. Compare book prices on Bookwire.com to buy books from the lowest price among top online book retailers
The aim of this article is to demonstrate the dummy variables for estimation seasonal effects in a time series, to use them as inputs in a regression model for obtaining quality predictions. Model parameters were estimated using the least square method. After fitting, special tests to determine, if the model is satisfactory, were employed. The application data were analyzed using the MATLAB computer program that performs these calculations.
The most common powder XRD refinement technique used today is based on the method proposed in the 1960s by Hugo Rietveld.[2] The Rietveld method fits a calculated profile (including all structural and instrumental parameters) to experimental data. It employs the non-linear least squares method, and requires the reasonable initial approximation of many free parameters, including peak shape, unit cell dimensions and coordinates of all atoms in the crystal structure. Other parameters can be guessed while still being reasonably refined. In this way one can refine the crystal structure of a powder material from PXRD data. The successful outcome of the refinement is directly related to the quality of the data, the quality of the model (including initial approximations), and the experience of the user. The Rietveld method is an incredibly powerful technique which began a remarkable era for powder XRD and materials science in general. Powder XRD is at heart a very basic experimental technique with ...
In this paper, recursive least squares method (RLSM) which is one of the adaptable classical methods is used. Forgetting factor approach is adapted to RLSM to obtain phase information of voltage signal belonging to an electric power network at first. Then responses of the algorithm are investigated for the signal which has zero magnitude for a specific time interval. And the examination of the frequency spectrum with RLSM method algorithm is done. Simulations are carried out by developing MATLABTM codes. Simulation results of method are presented ...
We invert 3775 relative P wave arrival times using the ACH damped least square method of Aki et al. (1977) to study upper mantle structure beneath the NE Iran continental collision zone. The data for this study were recorded by 17 three component broad-band stations operated from August 2006 to February 2008 along a profile from the center of Iranian Plateau, near Yazd, to the northeastern part of Iran on the Turan Platform just north of the Kopeh Dagh Mountains. The results confirm the previously known low velocity upper mantle beneath Central Iran. Our tomographic model reveals a deep high velocity anomaly. The surficial expressions of this anomaly are between the Ashkabad and Doruneh Faults, where the resolution and ray coverage are good. A transition zone in uppermost mantle is recognized under the Binalud foreland that we interpreted as suture zone between Iran and Turan platform. Our results indicate that Atrak Valley which is the boundary between the Binalud and Kopeh Dagh Mountains can ...
Creatinine is a metabolic waste product, removed from the blood by the kidneys, and excreted in the urine. The measurement of creatinine is used in the assessment and monitoring of many medical conditions as well as in the determination or adjustment of absorbed dosage of pesticides. Earlier models to predict 24-hour urinary creatinine used ordinary least squares regression and assumed that the subjects observations were uncorrelated. However, many of these studies had repeated creatinine measurements for each of their subjects. Repeated measures on the same subject frequently are correlated. Using data from the NIOSH-CDC Pesticide Dose Monitoring in Turf Applicators study, this thesis project built a model to predict 24-hour urinary creatinine using the Mixed Model methodology. A covariance structure, that permitted multiple observations for any one individual to be correlated, was identified and utilized. The predictive capabilities of this model were then compared to the earlier models
Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 for SPSS and SAS for model estimation, and annotated PROCESS v3 outputs. Using the principles of ordinary least squares regression, Andrew F. Hayes carefully explains procedures for testing hypotheses about the conditions under and the mechanisms by which causal effects operate, as well as the moderation of such mechanisms. Hayes shows how to estimate and interpret direct, indirect, and conditional effects; probe and visualize interactions; test questions about moderated mediation; and report different types of analyses. Data for all the examples are available on the companion website (www.afhayes.com), along with links to download PROCESS.
Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 for SPSS and SAS for model estimation, and annotated PROCESS v3 outputs. Using the principles of ordinary least squares regression, Andrew F. Hayes carefully explains procedures for testing hypotheses about the conditions under and the mechanisms by which causal effects operate, as well as the moderation of such mechanisms.
The objective of this study was to develop an agent based modeling (ABM) framework to simulate the behavior of patients who leave a public hospital emergency department (ED) without being seen (LWBS). In doing so, the study complements computer modeling and cellular automata (CA) techniques to simulate the behavior of patients in an ED. After verifying and validating the model by comparing it with data from a real case study, the significance of four preventive policies including increasing number of triage nurses, fast-track treatment, increasing the waiting room capacity and reducing treatment time were investigated by utilizing ordinary least squares regression. After applying the preventing policies in ED, an average of 42.14% reduction in the number of patients who leave without being seen and 6.05% reduction in the average length of stay (LOS) of patients was reported. This study is the first to apply CA in an ED simulation. Comparing the average LOS before and after applying CA with ...
A common application of the inverse Mills ratio (sometimes also called non-selection hazard) arises in regression analysis to take account of a possible selection bias. If a dependent variable is censored (i.e., not for all observations a positive outcome is observed) it causes a concentration of observations at zero values. This problem was first acknowledged by Tobin (1958), who showed that if this is not taken into consideration in the estimation procedure, an ordinary least squares estimation will produce biased parameter estimates.[6] With censored dependent variables there is a violation of the Gauss-Markov assumption of zero correlation between independent variables and the error term.[7] James Heckman proposed a two-stage estimation procedure using the inverse Mills ratio to correct for the selection bias.[8][9] In a first step, a regression for observing a positive outcome of the dependent variable is modeled with a probit model. The inverse Mills ratio must be generated from the ...
the least-squares method becomes invalid. The empirical Bayes method still works even if p ≫ n, but the residual variance is consumed by so many of the spurious effects. If all the estimated effects were included in the calculation of the genetic variance without using some kind of testing criterion, the overall epistatic variance would always dominate over the additive variance (a useless conclusion). This explains why significance tests have been conducted in this study.. The epistatic effects defined in our model are different from the orthogonal contrasts defined by Cockerham (1954) and recently reiterated by Kao and Zeng (2002) and Zeng et al. (2005). Kao and Zeng called Cockerhams epistatic effects the statistical parameters and the epistatic effects defined here the genetic parameters. The genetic parameters were also called physiological parameters by Cheverud and Routman (1995). The orthogonal contrasts can be expressed as linear functions of the genetic effects defined in our model. ...
In recent years there have been considerable new legislation and efforts by vehicle manufactures aimed at reducing pollutant emission to improve air quality in urban areas. Carbon monoxide is a major pollutant in urban areas, and in this study we analyze monthly carbon monoxide data from Valencia City, a representative Mediterranean city in terms of its structure and climatology. Temporal and spatial trends in pollution were recorded from a monitoring network that consisted of five monitoring sites. A multiple linear model, incorporating meteorological parameters, annual cycles, and random error due to serial correlation, was used to estimate the temporal changes in pollution. An analysis performed on the meteorologically adjusted data reveals a significant decreasing trend in CO concentrations and an annual seasonal cycle. The model parameters are estimated by applying the least-squares method. The standard error of the parameters is determined while taking into account the serial correlation ...
A solid-state based near-infrared (NIR) spectrometer is described and evaluated for the clinical measurements of glucose and urea. The development of a novel solid-state spectrometer advances noninvasive sensing technology by providing instrumentation capable of simple, robust, and reliable analytical measurements. The resulting solid-state systems promise the established features of NIR spectroscopy, including nondestructive, noninvasive, and reagent-less measurements. These features make solid-state NIR sensing attractive for bedside or near patient clinical applications associated with the treatment of diabetes and renal failure. A solid-state based optical micro-sensor is described and a preliminary evaluation for continuous glucose monitoring is detailed. Partial least-squares (PLS) and net analyte signal (NAS) calibration models are developed based on in vitro data to demonstrate the analytical features of selectivity, sensitivity and accuracy for the measurements of glucose, urea, and lactate in
This paper is concerned with model selection and model averaging procedures for partially linear single-index models. and over models chosen by AIC or BIC in terms of coverage probability and mean squared error. Our approach is further applied to real data from a male fertility study to explore potential factors related to sperm concentration and estimate the relationship between sperm concentration and monobutyl phthalate. which are assumed to be linearly related to the outcome. Various methods have been proposed in the literature for parameter estimators in the PLSIM. For example Carroll et al. (1997) studied a more general case and proposed one-step and fully iterated estimation procedures; Yu and Ruppert (2002) developed the penalized spline estimation procedure; Xia and H?ardle (2006) borrowed some ideas from dimension reduction (Xia et al. 1999 and then proposed the minimum average variance estimation (MAVE) method; Liang et al. (2010) proposed a profile least squares approach and obtained ...
Viral kinetic parameters were estimated with a bi-phasic mathematical model:. V(t) = (1-ε)pI(t) - cV(t). I(t) = (1- η)TV(t) - δI(t). V serum viral load, I productively infected cells, ε efficiency factor of blocking virus production, p viral production rate, c viral clearance rate, η efficiency factor of blocking de novo infection, β de novo infection rate, T uninfected target cells, δ rate of infected cell loss. Maximum-likelihood estimation for the viral kinetic parameters entailed fitting a nonlinear differential equation system via the least-squares approach from serum HBV DNA data. ...