TY - JOUR. T1 - Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks. AU - Petersen, Alexander. AU - Zhao, Jianyang. AU - Carmichael, Owen. AU - Müller, Hans Georg. PY - 2016/9/1. Y1 - 2016/9/1. N2 - In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich ...

A non-iterative spatial phase-shifting algorithm based on principal component analysis (PCA) is proposed to directly extract the phase from only a single spatial carrier interferogram. Firstly, we compose a set of phase-shifted fringe patterns from the original spatial carrier interferogram shifting by one pixel their starting position. Secondly, two uncorrelated quadrature signals that correspond to the first and second principal components are extracted from the phase-shifted interferograms by the PCA algorithm. Then, the modulating phase is calculated from the arctangent function of the two quadrature signals. Meanwhile, the main factors that may influence the performance of the proposed method are analyzed and discussed, such as the level of random noise, the carrier-frequency values and the angle of carrier-frequency of fringe pattern. Numerical simulations and experiments are given to demonstrate the performance of the proposed method and the results show that the proposed method is fast, ...

Time series is a series of observations over time. When there is one observation at each time instance, it is called a univariate time series (UTS), and when there are more than one observations, it is called a multivariate time series (MTS). While UTS datasets have been extensively explored, MTS datasets have not been broadly investigated. The techniques for UTS datasets, however, cannot be simply extended for MTS datasets, since multivariate time series is different from multiple univariate time series. That is, an MTS item may not be broken into multiple univariate time series and be separately analyzed, because this will result in the loss of the correlation information within the multivariate time series.; In this dissertation, we introduce a set of techniques for multivariate time series analysis based on principal component analysis (PCA). As a similarity measure for MTS datasets, we present Eros (Extended Frobenius norm). Eros computes the similarity between two MTS items by comparing ...

The aim of this study was to forecast the returns for the Stock Exchange of Thailand (SET) Index by adding some explanatory variables and stationary Autoregressive Moving-Average order p and q (ARMA (p, q)) in the mean equation of returns. In addition, we used Principal Component Analysis (PCA) to remove possible complications caused by multicollinearity. Afterwards, we forecast the volatility of the returns for the SET Index. Results showed that the ARMA (1,1), which includes multiple regression based on PCA, has the best performance. In forecasting the volatility of returns, the GARCH model performs best for one day ahead; and the EGARCH model performs best for five days, ten days and twenty-two days ahead.

When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has not the limitation of linear discriminant analysis (LDA) in the number of extracted features. In DA-PC1, the dominant structure of distribution is preserved by PC1 and the class separability is increased by DA. The experimental results show the good performance of DA-PC1 compared to some state-of-the-art feature extraction methods.

Kinetic modeling using a reference region is a common method for the analysis of dynamic PET studies. Available methods for outlining regions of interest representing reference regions are usually time-consuming and difficult and tend to be subjective; therefore, MRI is used to help physicians and experts to define regions of interest with higher precision. The current work introduces a fast and automated method to delineate the reference region of images obtained from an N-methyl-(11)C-2-(4-methylaminophenyl)-6-hydroxy-benzothiazole ((11)C-PIB) PET study on Alzheimer disease patients and healthy controls using a newly introduced masked volumewise principal-component analysis.. METHODS: The analysis was performed on PET studies from 22 Alzheimer disease patients (baseline, follow-up, and test/retest studies) and 4 healthy controls, that is, a total of 26 individual scans. The second principal-component images, which illustrate the kinetic behavior of the tracer in gray matter of the cerebellar ...

Given a set of points in Euclidean space, the first principal component corresponds to a line that passes through the multidimensional mean and minimizes the sum of squares of the distances of the points from the line. The second principal component corresponds to the same concept after all correlation with the first principal component has been subtracted from the points. The singular values (in Σ) are the square roots of the eigenvalues of the matrix XTX. Each eigenvalue is proportional to the portion of the "variance" (more correctly of the sum of the squared distances of the points from their multidimensional mean) that is associated with each eigenvector. The sum of all the eigenvalues is equal to the sum of the squared distances of the points from their multidimensional mean. PCA essentially rotates the set of points around their mean in order to align with the principal components. This moves as much of the variance as possible (using an orthogonal transformation) into the first few ...

I think that what you describe is a standard application of multivariate functional data clustering. In the context of multivariate functional data each data unit is treated as the relation of a $d$-dimensional stochastic (often Gaussian) process $X := ( X_1, \dots , X_d )$.. Jacques & Preda (the authors of the nice survey paper you attach) have (somewhat) recently published a paper on "Model-based clustering for multivariate functional data (2014)" which extends their earlier work on "Clustering multivariate functional data (2012)". Approximately at the same time Chiou et al. also on "Multivariate functional principal component analysis: A normalization approach (2014)". Note that the two approach are quite different; Chious approach has a particular (very flexible) parametric association between the curve-samples while Jacques & Preda is much more data-driven.. Both of these works are based on multivariate functional principal component analysis (MvFPCA). Earlier applications where alluded in ...

Aiming at the problem that the evaluation model had proposed by researchers to evaluate the drivability of a vehicle in the process of engine start to exist poor stability and poor accuracy. In this paper, a drivability evaluation model combined with principal component analysis and support vector r

Background: Bacteria employ a variety of adaptation strategies during the course of chronic infections. Understanding bacterial adaptation can facilitate the identification of novel drug targets for better treatment of infectious diseases. Transcriptome profiling is a comprehensive and high-throughput approach for characterization of bacterial clinical isolates from infections. However, exploitation of the complex, noisy and high-dimensional transcriptomic dataset is difficult and often hindered by low statistical power. Results: In this study, we have applied two kinds of unsupervised analysis methods, principle component analysis (PCA) and independent component analysis (ICA), to extract and characterize the most informative features from transcriptomic dataset generated from cystic fibrosis (CF) Pseudomonas aeruginosa isolates. ICA was shown to be able to efficiently extract biological meaningful features from the transcriptomic dataset and improve clustering patterns of CF isolates. ...

NOTE: Where studies included discovery and validation cohorts, diagnostic metrics of the validation set included for analysis.. Abbreviations: EAC, esophageal adenocarcinoma; ESCC, esophageal squamo-cellular carcinoma; GAC, gastric adenocarcinoma; CRC, colorectal adenocarcinoma; UPLC-TQMS, ultra-performance liquid chromatography-triple quadrupole mass spectrometry; NMR, nuclear magnetic resonance spectroscopy; ESI-TOFMS, electrospray ionization time-of-flight mass spectrometry; RRLC, rapid relaxing liquid chromatography; GC-MS, gas chromatography mass spectrometry; HPLC, high-performance liquid chromatography; FTICR-MS, Fourier transform ion cyclotron mass spectrometry; MS/MS, tandem mass spectrometry; TQMRM, triple quadrupole multiple reaction monitoring; DI, direct ionization; SPME, solid phase microextraction; PLS-DA, partial least squares discriminant analysis; ROC, receiver operating characteristic curve; PCA, principle component analysis; OPLS-DA, orthogonal projection to latent structures ...

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.

TY - JOUR. T1 - All sparse PCA models are wrong, but some are useful. Part I. T2 - Computation of scores, residuals and explained variance. AU - Camacho, J.. AU - Smilde, A. K.. AU - Saccenti, E.. AU - Westerhuis, J. A.. PY - 2020/1/15. Y1 - 2020/1/15. N2 - Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity with the ultimate goal of improving data interpretation. When moving from PCA to sPCA, there are a number of implications that the practitioner needs to be aware of. A relevant one is that scores and loadings in sPCA may not be orthogonal. For this reason, the traditional way of computing scores, residuals and variance explained that is used in the classical PCA can lead to unexpected properties and therefore incorrect interpretations in sPCA. This also affects how sPCA components should be visualized. In this paper we illustrate this problem both theoretically and ...

It is important to manage leaks in water distribution systems by smart water technologies. In order to reduce the water loss, researches on the main factors of water pipe network affecting non-revenue water (NRW) are being actively carried out. In recent years, research has been conducted to estimate NRW using statistical analysis techniques such as Artificial Neural Network (ANN) and Principle Component Analysis (PCA). Research on identifying factors that affect NRW in the target area is actively underway. In this study, Principle components selected through Multiple Regression Analysis are reclassified and applied to NRW estimation using PCA-ANN. The results show that the principal components estimated through PCA are connected to the NRW estimation using ANN. The detailed NRW estimation methodology presented through the study, as a result of simulating PCA-ANN after selecting statistically significant factors by MRA, forward method showed higher NRW estimation accuracy than other MRA methods.

Background: In this paper we apply the principal-component analysis filter (Hotelling filter) to reduce noise fromdynamic positron-emission tomography (PET) patient data, for a number of different radio-tracer molecules. Wefurthermore show how preprocessing images with this filter improves parametric images created from suchdynamic sequence.We use zero-mean unit variance normalization, prior to performing a Hotelling filter on the slices of a dynamictime-series. The Scree-plot technique was used to determine which principal components to be rejected in thefilter process. This filter was applied to [11C]-acetate on heart and head-neck tumors, [18F]-FDG on liver tumors andbrain, and [11C]-Raclopride on brain. Simulations of blood and tissue regions with noise properties matched to realPET data, was used to analyze how quantitation and resolution is affected by the Hotelling filter. Summing varyingparts of a 90-frame [18F]-FDG brain scan, we created 9-frame dynamic scans with image statistics ...

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.

The common task in matrix completion (MC) and robust
principle component analysis (RPCA) is to recover a low-rank matrix
from a given data matrix. These problems gained great attention from various areas
in applied sciences recently, especially after the publication of the pioneering
works of Candès et al.. One fundamental result in MC and RPCA is
that nuclear norm based convex optimizations lead to the exact low-rank matrix
recovery under suitable conditions. In this paper, we extend this result by showing that strongly convex optimizations can guarantee
the exact low-rank matrix recovery as well. The result in this paper not only
provides sufficient conditions under which the strongly convex models lead to the exact low-rank matrix recovery,
but also guides us on how to choose suitable parameters in practical algorithms.

A principal components analysis was carried out on male crania from the northeast quadrant of Africa and selected European and other African series. Individuals, not predefined groups, were the units of study, while nevertheless keeping group membership in evidence. The first principal component seems to largely capture size variation in crania from all of the regions. The same general morphometric trends were found to exist within the African and European crania, although there was some broad separation along a cline. Anatomically, the second principal component captures predominant trends denoting a broader to narrower nasal aperture combined with a similar shape change in the maxilla, an inverse relation between face-base lengths (projection) and base breadths, and a decrease in anterior base length relative to base breadth. The third principal component broadly describes trends within Africa and Europe: specifically, a change from a combination of a relatively narrower face and longer vault, ...

Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score ...

The classical functional linear regression model (FLM) and its extensions, which are based on the assumption that all individuals are mutually independent, have been well studied and are used by many researchers. This independence assumption is sometimes violated in practice, especially when data with a network structure are collected in scientific disciplines including marketing, sociology and spatial economics. However, relatively few studies have examined the applications of FLM to data with network structures. We propose a novel spatial functional linear model (SFLM), that incorporates a spatial autoregressive parameter and a spatial weight matrix into FLM to accommodate spatial dependencies among individuals. The proposed model is relatively flexible as it takes advantage of FLM in handling high-dimensional covariates and spatial autoregressive (SAR) model in capturing network dependencies. We develop an estimation method based on functional principal component analysis (FPCA) and maximum

Chaka sheep, named after Chaka Salt Lake, are adapted to a harsh, highly saline environment. They are known for their high-grade meat quality and are a valuable genetic resource in China. Furthermore, the Chaka sheep breed has been designated a geographical symbol of agricultural products by the Chinese Ministry of Agriculture. The genomes of 10 Chaka sheep were sequenced using next-generation sequencing, and compared to that of additional Chinese sheep breeds (Mongolian: Bayinbuluke and Tan; Tibetan: Oula sheep) to explore its population structure, genetic diversity and positive selection signatures. Principle component analysis and a neighbor-joining tree indicated that Chaka sheep significantly diverged from Bayinbuluke, Tan, and Oula sheep. Moreover, they were found to have descended from unique ancestors (K = 2 and K = 3) according to the structure analysis. The Chaka sheep genome demonstrated comparable genetic diversity from the other three breeds, as indicated by observed heterozygosity (Ho),

This course is in two halves: machine learning and complex networks. We will begin with an introduction to the R language and to visualisation and exploratory data analysis. We will describe the mathematical challenges and ideas in learning from data. We will introduce unsupervised and supervised learning through theory and through application of commonly used methods (such as principle components analysis, k-nearest neighbours, support vector machines and others). Moving to complex networks, we will introduce key concepts of graph theory and discuss model graphs used to describe social and biological phenomena (including Erdos-Renyi graphs, small-world and scale-free networks). We will define basic metrics to characterise data-derived networks, and illustrate how networks can be a useful way to interpret data. This level 7 (Masters) version of the module will have additional extension material for self-study incorporated into the projects. This will require a deeper understanding of the subject ...

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and ...

We use spectral methods (SVD) to building statistical language models. The resulting vector models of language are then used to predict a variety of properties of words including their entity type (E.g., person, place, organization ...), their part of speech, and their "meaning" (or at least their word sense). Canonical Correlation Analysis, CCA, a generalization of Principle Component Analysis (PCA), gives context-oblivious vector representations of words. More sophisticated spectral methods are used to estimate Hidden Markov Models (HMMs) and generative parsing models. These methods give state estimates for words and phrases based on their contexts, and probabilites for word sequences. These again can be used to imrpove performance on many NLP tasks. Core to this work is the use of the Eigenword, a real-valued vector associated with a word that captures its meaning in the sense that distributionally similar words have similar eigenwords. Eigenwords are computed as the singular vectors of the ...

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Enhancers and promoters are cis-acting regulatory elements associated with lineage-specific gene expression. Previous studies showed that different categories of active regulatory elements are in regions of open chromatin, and each category is associated with a specific subset of post-translationally marked histones. These regulatory elements are systematically activated and repressed to promote commitment of hematopoietic stem cells along separate differentiation paths, including the closely related erythrocyte (ERY) and megakaryocyte (MK) lineages. However, the order in which these decisions are made remains unclear. To characterize the order of cell fate decisions during hematopoiesis, we collected primary cells from mouse bone marrow and isolated 10 hematopoietic populations to generate transcriptomes and genome-wide maps of chromatin accessibility and histone H3 acetylated at lysine 27 binding (H3K27ac). Principle component analysis of transcriptional and open chromatin profiles demonstrated that

TY - JOUR. T1 - Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data. AU - Huang, Lei. AU - Reiss, Philip T.. AU - Xiao, Luo. AU - Zipunnikov, Vadim. AU - Lindquist, Martin. AU - Crainiceanu, Ciprian M. PY - 2017/4/1. Y1 - 2017/4/1. N2 - Many modern neuroimaging studies acquire large spatial images of the brain observed sequentially over time. Such data are often stored in the forms of matrices. To model these matrix-variate data we introduce a class of separable processes using explicit latent process modeling. To account for the size and two-way structure of the data, we extend principal component analysis to achieve dimensionality reduction at the individual level. We introduce necessary identifiability conditions for each model and develop scalable estimationprocedures.Themethodismotivatedbyandappliedtoafunctionalmagneticresonanceimaging study designed to analyze the relationship between pain and brain ...

To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. Twenty-two baseline variables were obtained from 154 patients with IgG4-RD. Based on principal component analysis (PCA), patients with IgG4-RD were classified into different subgroups using cluster analysis. Additionally, IgG4-RD composite score (IgG4-RD CS) as a comprehensive score was calculated for each patient by principal component evaluation. Multiple linear regression was used to establish the

Implements biplot (2d and 3d) of multivariate data based on principal components analysis and diagnostic tools of the quality of the reduction.. ...

The HPPRINCOMP procedure is a high-performance procedure that performs principal component analysis. It is a high-performance version of the PRINCOMP procedure in SAS/STAT software. PROC HPPRINCOMP accepts raw data as input and can create output data sets that contain eigenvalues, eigenvectors, and standardized or unstandardized principal component scores. Principal component analysis is a multivariate technique for examining relationships among several quantitative variables. The choice between using factor analysis and using principal component analysis depends in part on your research objectives. You should use the HPPRINCOMP procedure if you are interested in summarizing data and detecting linear relationships. You can use principal component analysis to reduce the number of variables in regression, clustering, and so on. Principal component analysis was originated by Pearson (1901) and later developed by Hotelling (1933). The application of principal components is discussed by Rao (1964); ...

The present study addresses the challenge of identifying the features of the centre
of pressure (CoP) trajectory that are most sensitive to postural...

Downloadable! This article documents and examines the integration of grain markets in Europe across the early modern/late modern divide and across distances and regions. It relies on principal component analysis to identify market structures. The analysis finds that a European market emerged only in the nineteenth century, but the process had earlier roots. In early modern times a fall in trading costs was followed by an increase in market efficiency. Gradually expanding processes of integration unfolded in the long-run. Early modern regional integration was widespread but uneven, with North-Western Europe reaching high levels of integration at a particularly early stage. Low-land European markets tended to be larger and better integrated than in land-locked Europe, especially within large, centralised states. In the nineteenth century, national markets grew in old states, but continental and domestic dynamics had become strictly linked.

This article describes the major statistical analyses used in a large-scale follow-up study of prelingually deaf children implanted before 5 yrs of age. The data from this longitudinal project posed a number of challenges that required a compromise among statistical sophistication, ease of interpretation, consistency with analyses used following the initial wave of data collection, and attention to limited sample size and missing data. Primary analyses were based on principal components analysis to form composite measures of highly correlated variables followed by hierarchical multiple regression to determine the contribution of predictor sets ordered to reflect important causal assumptions and conceptual questions ...

Objective: To develop a psychometric questionnaire to measure psychological barriers to insulin treatment in patients with type 2 diabetes.. Research Design and Methods: Scale development was based on principal component analyses in two cross-sectional studies of insulin-naïve patients with type 2 diabetes. The structure of the questionnaire was developed in the first sample of 448 patients and subsequently cross-validated in an independent sample of 449 patients.. Results: Analyses in the first sample yielded five components that accounted for 74.5% of the variance based on 14 items and led to the following subscales: Fear of injection and self-testing, Expectations regarding positive insulin-related outcomes, Expected hardship from insulin treatment, Stigmatization by insulin injections, and Fear of hypoglycemia. In addition, an overall sum score of all values was calculated. The structure of the questionnaire was cross-validated in the second sample with almost identical component loadings ...

Principal components analysis (PCA)33,34 was used to investigate the morphological affinities of the pre-5000 BP sample and also to characterize their primary morphological traits. All analyses were carried out on untransformed data (preserving size). The computation of the principal components (PCs) was done via the correlation matrix. PCA has the advantage of being able to reduce a large data set of (possibly) correlated variables into a (smaller) number of uncorrelated variables, the PCs. Analysing the PCs makes it easier to identify meaningful underlying variables that distinguish crania from one another. PCs may be plotted against each other, to visualize morphological relationships. Specimens that are morphologically similar occupy similar multivariate space. PCA is particularly useful in the context of this study in that it allows for the evaluation of size and size-related shape variation within the study sample. In biological studies, the first principal component commonly reflects ...

A more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health. An environmental quality index (EQI) for all counties in the U.S. was developed. The EQI was developed in four parts: domain identification; data source acquisition; variable construction; and data reduction. Five environmental domains (air, water, land, built and sociodemographic) were recognized. Within each domain, data sources were identified; each was temporally (years 2000-2005) and geographically (county) restricted. Variables were constructed for each domain and assessed for missingness, collinearity, and normality. Domain-specific data reduction was accomplished using principal components analysis (PCA), resulting in domain-specific indices. Domain-specific indices were then combined into an overall EQI using PCA. In each PCA procedure, the first principal component was retained. Both domain-specific indices and overall EQI were stratified

Abstract(#br)The aim of this study is to develop new algorithms of the column ozone (O 3 ) in Peninsular Malaysia using statistical methods. Four regression equations, denoted as O 3 NEM, O 3 SWM, (PCA1) O 3 NEM season, and (PCA2) O 3 SWM season, were developed. Multiple regression analysis (MRA) and principal component analysis (PCA) methods were utilized to achieve the objectives of the study. MRA was used to generate regression equations for O 3 NEM and O 3 SWM, whereas a combination of the MRA and PCA methods were used to generate regression equations for PCA1 and PCA2. The results of the best regression equations for the column O 3 through MRA by using four of the independent variables were highly correlated (R = 0.811 for SWM, R = 0.803 for NEM) for the six-year (2003-2008) data.... However, the result of fitting the best equations for the O 3 data using four of the independent variables gave approximately the same R values (≈0.83) for both the NEM and SWM seasons using the combined MRA ...

Downloadable! Risk management technology applied to high dimensional portfolios needs simple and fast methods for calculation of Value-at-Risk (VaR). The multivariate normal framework provides a simple off-the-shelf methodology but lacks the heavy tailed distributional properties that are observed in data. A principle component based method (tied closely to the elliptical structure of the distribution) is therefore expected to be unsatisfactory. Here we propose and analyze a technology that is based on Independent Component Analysis (ICA). We study the proposed ICVaR methodology in an extensive simulation study and apply it to a high dimensional portfolio situation. Our analysis yields very accurate VaRs.

Purpose: Metabonomics is a well-developed platform for studying systems biology and clinical diagnosis. In this study, we investigated a metabonomic approach to prognostic evaluation of LPS-induced ALI by HR NMR spectroscopy.. Methods: The mice model of ALI was established by intratracheal instillation of LPS (5mg/kg) for 4 hours with the saline in control mice. The mice of dexamethasone (DEX) treatment group were intraperitoneally treated with DEX while intratracheal instillation of LPS. HR 1H NMR spectroscopy in combination with pattern recognition methods was applied to study 15 lung tissues extract samples of mice.. Results: The lung injury score were significantly increased in ALI mice compared to control group and DEX treatment markedly decreased the lung injury score. The first principal component (PC1) shows a good separation between the ALI group and control (or DEX treatment) groups. The metabolites showed clear differentiation between groups mainly included valine (Val, δ 1.06), ...

In the context of functional data analysis, functional linear regression serves as a fundamental tool to handle the relationship between a scalar response and a functional covariate. With the aid of Karhunen-Loève expansion of a stochastic process, a functional linear model can be written as an infinite linear combination of functional principal component scores. A reduced form is fitted in practice for dimension reduction; it is essentially converted to a multiple linear regression model.. Though the functional linear model is easy to implement and interpret in applications, it may suffer from an inadequate fit due to this specific linear representation. Additionally, effects of scalar predictors which may be predictive of the scalar response are neglected in the functional linear model.. Prediction accuracy can be enhanced greatly by incorporating effects of these scalar predictors.. In this talk, we propose a functional semiparametric additive model, which models the effect of a functional ...

We begin by considering the first principal component (Figs. 5, 7). On average, the first PC accounted for 52, 50, and 40% of the variance in the memory-guided, virtual, and real conditions. The main kinematic features of the examples shown in Figures 1 and 2 were well captured by the first PC. Specifically, all the mcp and pip joints tended to extend and flex together during the movement, simultaneously reaching a maximum excursion. At the same time, the digits were gradually abducted and later adducted toward the end of the reach. In contrast, abduction of the thumb tended to be monotonic, and there was little motion at the thumbs mcp and ip joints. This general pattern of coordinated motion of the hand can be appreciated in Figure 7 (top row), where we have reconstructed hand posture at different epochs of the movement, adding the first PC to the average posture at movement onset. The reconstruction shows snapshots of the movement that would occur if only the joint synergy represented by PC1 ...

TY - JOUR. T1 - A principal component analysis approach to correcting the knee flexion axis during gait. AU - Jensen, Elisabeth. AU - Lugade, Vipul. AU - Crenshaw, Jeremy. AU - Miller, Emily. AU - Kaufman, Kenton R. PY - 2016. Y1 - 2016. N2 - Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotomies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post-hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms. Gait data were collected on twelve healthy subjects using standard marker placement as well as intentionally misplaced lateral knee markers. The efficacy of the algorithm was assessed by quantifying the reduction in knee angle errors. Crosstalk error was quantified from the coefficient of determination (r 2) between knee flexion and adduction angles. Mean rotation offset error (α o) was ...

Matlab and Mathematica & Statistics Projects for $30 - $250. Main demanded tasks to solve a small exercise in Mathematica: - Analysis of data sets / descriptive statistics - Perform clustering algorithms - Perform PCA analysis / principal components - Create t...

The initial sequence reads were filtered to remove artifactual sequence reads (i.e., reads containing two or more different tags, no tags, primers in the middle of sequence reads, or without a primer sequence). The filtered sequences were then searched against the Ribosomal Database Project II (RDPII) database [7], using the online program SEQMATCH (http://rdp.cme.msu.edu/seqmatch/ seqmatch_intro.jsp) and a threshold setting of 90%, to assign a genus to each sequence. Diversity statistics and apportionment of variation based on the frequency distribution of genera within and between individuals were calculated with Arlequin 3.1 [8], while pairwise correlation analysis and principal component analysis (PCA) were carried out using STATISTICA 6.1 (StatSoft, Inc.) [9]. Mann-Whitney U tests [10] were used to compare distributions of correlation coefficients. Rarefaction analysis was carried out using the Resampling Rarefaction 1.3 software (http://www.uga.edu/ ,strata/software/index.html ). UniFrac ...

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Download Free Full-Text of an article STUDY OF KARYOTYPIC CHARACTERISTICS OF POPULATIONS OF STIPAGROSTIS PENNATA USING PRINCIPAL COMPONENTS ANALYSIS

During the past decade, regional changes in the dynamics of the Atlanto-Iberian stock of sardine, and its exploitation by Portuguese and Spanish purse-seine fisheries, have increased the uncertainties in estimated trends of spawning biomass, stock abundance, and fishing mortality. Together with recent evidence for lack of discontinuities in the distribution of sardine eggs at the edges of the stock area, this casts doubts on the hypothesis that the stock is a panmictic, closed population. Sardine morphometric data (truss variables and landmark data) from 14 samples spanning the northeastern Atlantic and the western Mediterranean were analysed by multivariate and geometric methods. The analyses explored the homogeneity of sardine shape within the area studied, as well as its relation to that of adjacent and distant populations (Azores and northwestern Mediterranean). Principal components analysis on size-corrected truss variables and cluster analysis of mean fish shape using landmark data ...

Tail biting in pigs is a widespread problem in intensive pig farming. The tendency to develop this damaging behaviour has been suggested to relate to serotonergic functioning and personality characteristics of pigs. We investigated whether tail biting in pigs can be associated with blood serotonin and with their behavioural and physiological responses to novelty. Pigs (n = 480) were born in conventional farrowing pens and after weaning at four weeks of age they were either housed barren (B) or in straw-enriched (E) pens. Individual pigs were exposed to a back test and novel environment test before weaning, and after weaning to a novel object (i.e. bucket) test in an unfamiliar arena. A Principal Component Analysis on behaviours during the tests and salivary cortisol (novel object test only) revealed five factors for both housing systems, labeled Early life exploration, Near bucket, Cortisol, Vocalizations & standing alert, and Back test activity. Blood samples were taken at 8, 9 and 22 weeks

Description: In this research, hyperspectral and multispectral images were utilized for vegetation studies in the greenbelt corridor near Denton. EO-1 Hyperion was the hyperspectral image and Landsat Thematic Mapper (TM) was the multispectral image used for this research. In the first part of the research, both the images were classified for land cover mapping (after necessary atmospheric correction and geometric registration) using supervised classification method with maximum likelihood algorithm and accuracy of the classification was also assessed for comparison. Hyperspectral image was preprocessed for classification through principal component analysis (PCA), segmented principal component analysis and minimum noise fraction (MNF) transform. Three different images were achieved after these pre-processing of the hyperspectral image. Therefore, a total of four images were classified and assessed the accuracy. In the second part, a more precise and improved land cover study was done on ...

This work is geared towards detecting and solving the problem of multicolinearity
in regression analysis. As such, Variance Inflation Factor (VIF) and the
Condition Index (CI) were used as measures of such detection. Ridge Regression
(RR) and the Principal Component Regression (PCR) were the two other approaches
used in modeling apart from the conventional simple linear regression. For the
purpose of comparing the two methods, simulated data were used. Our task is to
ascertain the effectiveness of each of the methods based on their respective
mean square errors. From the result, we found that Ridge Regression (RR) method is better than principal component regression when multicollinearity exists
among the predictors.