Analytical, Diagnostic and Therapeutic Techniques and EquipmentPhenomena and ProcessesDisciplines and OccupationsInformation ScienceHealth Care
Least-Squares AnalysisPrincipal Component AnalysisDiscriminant AnalysisAlgorithmsMetabolomicsReproducibility of ResultsCluster AnalysisModels, StatisticalComputer SimulationMetabolomeData Interpretation, StatisticalFactor Analysis, StatisticalGene Expression ProfilingModels, GeneticOligonucleotide Array Sequence Analysis
EstimationAlgorithmsLassoPartialInferenceSparseVarianceEmpiricalDecompositionQuadraticCriterionClusterProbabilityMethodsOrdinaryCorrelationTensorClassificationLogistic regressionUncertaintyLatentProceduresMultivariateLower boundExploratoryRegression analysisFactor analysisModelDiffuse reflectanceInvestigateWidelyApproachQuantitativeVariancesMethodExperimentalDiscriminantStatistical analysisModelsLinearComparativeStatisticsData analysisResults
Estimation5
  • To minimize the impact of these missing values, several imputation algorithms including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute), Local Least Square Impute (LLSImpute), and K-Nearest Neighbour (KNN) have been proposed. (open.ac.uk)
  • Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism. (samuelbhopkins.com)
  • Efficient Bayesian estimation from few samples: community detection and related problems. (samuelbhopkins.com)
  • The insights conveyed through our analysis of nonconvex optimization might have implications for other tensor estimation problems. (nsf.gov)
  • From the different methods that exist (point estimate, estimate intervals, or Bayesian estimation) we focus on the point estimate. (upc.edu)
Algorithms3
  • Contains Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. (nuget.org)
  • Fast spectral algorithms from sum-of-squares proofs: tensor decomposition and planted sparse vectors. (samuelbhopkins.com)
  • Some of the topics covered include bootstrapping, ensemble methods such as boosting and random forests, unsupervised machine learning methods such as principal components analysis and clustering algorithms as well as applications of machine learning methods to problems that are relevant for business and economics, such as causal inference and text analysis. (lu.se)
Lasso3
  • Matlab Code (rar file) for Bayesian adaptive Lasso. (warwick.ac.uk)
  • Three different calibration techniques were tested, namely the Partial Least Square Regression (PLSR), the combinations wavelet transformation/Elastic net and wavelet transformation/Supervised Principal Component (SPC) regression/ Least Absolute Shrinkage and Selection Operator (LASSO), a kind of preconditioned LASSO. (sisef.it)
  • Some of the topics covered are classification based on logistic regression, model selection using information criteria and cross-validation, shrinkage methods such as lasso, ridge regression and elastic nets, dimension reduction methods such as principal components regression and partial least squares, and neural networks. (lu.se)
Partial5
  • Models include: linear mixed-effects models and nonlinear mixed-effects models Hidden Markov models Factor analysis Item response theory Analysis and inference methods include: Principal component analysis Instrumented principal component analysis Partial least squares regression Latent semantic analysis and probabilistic latent semantic analysis EM algorithms Metropolis-Hastings algorithm Bayesian statistics is often used for inferring latent variables. (wikipedia.org)
  • Confounding Dependent and independent variables Errors-in-variables models Evidence lower bound Factor analysis Intervening variable Latent variable model Item response theory Partial least squares path modeling Partial least squares regression Proxy (statistics) Rasch model Structural equation modeling Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. (wikipedia.org)
  • Contains probability distributions, statistical models and methods such as Linear and Logistic regression, Hidden Markov Models, (Hidden) Conditional Random Fields, Principal Component Analysis, Partial Least Squares, Discriminant Analysis, Kernel methods and functions and many other related techniques. (nuget.org)
  • Other methods, such as Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) overcome these problems by deriving a small number of linear combinations of the predictors and using these instead of the original variables to predict the outcome. (sisef.it)
  • We present SRGS, SPLS (sparse partial least squares)-based recursive gene selection, to infer GRNs from bulk or single-cell expression data. (biomedcentral.com)
Inference1
Sparse1
Variance6
  • introduction to analysis of variance. (uoguelph.ca)
  • Variance based sensitivity analysis of model output. (crossref.org)
  • Least square regression: normality and independence of residuals, significance of coefficients, analysis of variance, coefficients of determination and linear correlation, multicollinearity, confidence and prediction limits. (studwiz.com)
  • The main objective of the section is to know the procedures associated with the analysis of variance (ANOVA terminology in English) and when is useful to be applied.This activity also introduces MANOVA, as a technique useful when there are two or more dependent variables. (upc.edu)
  • An important technique for analyzing the effect of categorical factors on a response is to perform an Analysis of Variance. (alfasoft.com)
  • Statgraphics Technologies products provide several procedures for performing an analysis of variance: One-Way ANOVA, Multifactor ANOVA, Variance Components Analysis, General Linear Models, and Repeated Measures ANOVA. (alfasoft.com)
Empirical1
  • To this end, we employ a number of statistical methods and diagnostic tools, including Bayesian calibration, Bayesian model averaging, chi-square correlation analysis, principal component analysis, and empirical coverage probability. (arxiv.org)
Decomposition1
  • QPINT1D - One dimensional numerical adaptive integration of IDL function or expression QRFAC - Linear least squares using QR decomposition QUINTERP - Perform quintic tabulation of a tabulated function. (nasa.gov)
Quadratic1
  • QUADTERP - Quadratic interpolation of X,Y vectors onto a new X grid RANDOMCHI - Generate chi-square distributed random variables RANDOMDIR - Generate Dirichlet-distributed random variables RANDOMGAM - Generate random numbers from a gamma distribution. (nasa.gov)
Criterion3
  • Cluster analysis (CA) delineated natural delirium and nondelirium reference groups using DRS-R98 items and then diagnostic systems' performance were evaluated against the CA-defined groups using logistic regression and crosstabs for discriminant analysis (sensitivity, specificity, percentage of subjects correctly classified by each diagnostic system and their individual criteria, and performance for each system when excluding each individual criterion are reported). (biomedcentral.com)
  • Try out all possible subsets of variables and pick the one that gives a regression with the smallest Bayesian information criterion (BIC) value. (stackexchange.com)
  • The number of classes was identified by the Bayesian information criterion and entropy. (biomedcentral.com)
Cluster2
  • The clusters are chosen as local minima of the sum of square Euclidean distances from each point to its nearest cluster center. (itk.org)
  • We compare four DSM and ICD delirium diagnostic criteria versions, which were developed by consensus of experts, with a phenomenology-based natural diagnosis delineated using cluster analysis of delirium features in a sample with a high prevalence of dementia. (biomedcentral.com)
Probability1
  • Thus the result of analysis is a tabular bivariate probability distribution. (itk.org)
Methods3
  • Bayesian methods: naive methods, Bayesian networks. (studwiz.com)
  • Gauss-Newton and Levenberg-Marquardt iterative methods for solution of nonlinear least squares problems. (gu.se)
  • This course teaches the basics of machine learning and it does so by focusing on those methods that build in one way or another on standard regression analysis. (lu.se)
Ordinary2
  • High-dimensional ordinary least-squares projection for screening variables ( http://arxiv.org/abs/1506.01782 ). (warwick.ac.uk)
  • Covariate-adjusted treatment effects from ordinary least squares regression with control group (simulated white male physician). (jamanetwork.com)
Correlation4
  • We then use the chi-square correlation framework to analyze the 14-parameter Skyrme energy density functional calibrated using homogeneous and heterogeneous datasets. (arxiv.org)
  • curve fitting, the method of least squares and correlation. (uoguelph.ca)
  • Bivariate analysis: graphical analysis, measures of correlation, contingency tables. (studwiz.com)
  • Multivariate analysis: graphical analysis, measures of correlation. (studwiz.com)
Tensor1
Classification2
  • Linear classification with least squares is sensitive to outliers, contrary to logisitic regression. (younesse.net)
  • Nous proposons une classification des données manquantes en deux catégories Missing At Random et Not Missing At Random pour les modèles à variables latentes suivant le modèle décrit par D. Rubin. (inrae.fr)
Logistic regression1
  • The differences in prognostic ability among the AKI subphenotypes were evaluated by logistic regression analysis. (biomedcentral.com)
Uncertainty2
  • The advantage of Bayesian model averaging for improving uncertainty quantification is demonstrated. (arxiv.org)
  • This section will introduce the student to use the techniques of operations research for systems analysis for making quantitative decision in the presence of uncertainty through their representation in terms of queuing models and simulation. (upc.edu)
Latent4
  • 5 ] reported diagnostic accuracies for DSM-III, DSM-III-R, DSM-IV, and ICD-10 delirium criteria using latent class analysis (a latent variable model to delineate latent discrete variables from observed discrete criteria that allow describing accuracy among them). (biomedcentral.com)
  • Latent class analysis was used to identify distinct, latent clusters based on 40 food groups. (biomedcentral.com)
  • Latent class analysis (LCA) was used to explore the potential subphenotypes of AKI in critically ill patients with cardiovascular diseases. (biomedcentral.com)
  • Latent class analysis (LCA), a popular method based on multidimensional data, is used to identify potential heterogeneity among individuals. (biomedcentral.com)
Procedures1
  • Eight procedures (e.g. linmix_err.pro) were supplied by Brandon Kelly (U. of Arizona) as part of Bayesian approach to linear regression (astro-ph/0705.2774) Additional IDL mathematics and statistics procedures were developed by Henry Freudenreich (Hughes STX), The most well tested of these are available in the /robust directory, while the remainer must be downloaded separately from the contrib/freudenreich directory. (nasa.gov)
Multivariate1
Lower bound1
  • A nearly tight sum-of-squares lower bound for the planted clique problem. (samuelbhopkins.com)
Exploratory2
  • In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. (researchgate.net)
  • Exploratory Data Analysis refers to a set of techniques originally developed by John Tukey to display data so that interesting features will become apparent. (alfasoft.com)
Regression analysis1
  • In the late 1800s, Francis Galton, a British statistician, and eugenicist, popularized the use of regression analysis to predict the performance of racehorses based on their parentage and other factors. (freedomtoascend.com)
Factor analysis1
  • The "Big Five personality traits" have been inferred using factor analysis. (wikipedia.org)
Model5
  • Using a Bayesian framework, we investigate the structure of the 4-parameter Liquid Drop Model by considering discrepant mass domains for calibration. (arxiv.org)
  • This table serves as a Bayesian-style prior model when assessing a set of observations. (itk.org)
  • Try to think of a statistical analysis not as a fishing expedition but rather through reasoned model specification. (stackexchange.com)
  • We then established multi-model ensemble Bayesian model averaging (BMA). (aaqr.org)
  • Compared with the CMAQ model, the root-mean-square error of the SO 2 , NO 2 and CO forecasts by BMA were reduced by 29, 33 and 39%, respectively. (aaqr.org)
Diffuse reflectance1
  • Chemometric analysis applied to diffuse reflectance spectroscopy is increasingly proposed as an effective and accurate methodology to predict soil physical, chemical and biological properties. (sisef.it)
Investigate1
  • Together with the standard NRMS error metric, the True Positive (TP) rate of the significant genes selection, biological significance of the selected genes and the statistical significance test results are presented to investigate the impact of missing values on subsequent biological analysis. (open.ac.uk)
Widely3
  • Gene expression data is widely used in various post genomic analyses. (open.ac.uk)
  • Bayesian theory is also widely used in evolutionary biology, and many of my colleagues are quite well versed in it, which is to say I'm a little jealous of their superior mathematical knowledge. (blogspot.com)
  • Social preference weights for EQ-5D health states have been widely used in economic evaluations, specifically for use as the adjustment index used to compute quality-adjusted life years (QALYs) in cost-utility analysis. (bvsalud.org)
Approach2
  • MINF_BRACKET - Find 3 points which bracket the minimum of a function MINF_CONJ_GRAD - Find local minimum of a scalar valued function of several variables using conjugate gradient method MINF_PARABOLIC - Minimize a function using Brent's method with parabolic interpolation MINF_PARABOL_D - Minimize a function using Brent's method with derivatives MLINMIX_ERR : Bayesian approach to linear regression with errors in both X and Y and multiple independent variables. (nasa.gov)
  • Bayesian approach. (gu.se)
Quantitative1
  • Our mission is to provide cost effective, state-of-the-art expertise, methodology and instrumentation for high-throughput quantitative analysis of compounds such as metabolites, lipids, steroids, drug molecules and environmental contaminants in biological fluids and complex matrices. (ccrc-hauner.de)
Variances1
  • One Sample Analysis, Outlier Identification, Comparing Two Independent Samples, Comparing Two Paired Samples, Comparing Multiple Samples, Comparing Rates and Proportions, Equivalence and Noninferiority Tests for Means, Equivalence and Noninferiority Tests for Variances, and Power Transformations. (alfasoft.com)
Method2
  • The least squares method and its properties under several hypothetical settings. (unibocconi.eu)
  • Principal components method. (unibocconi.eu)
Experimental1
  • Our application of multi-omics analyses in experimental and field settings robustly predicts Bd disease dynamics and identifies novel candidate biomarkers of infection. (biomedcentral.com)
Discriminant1
Statistical analysis2
  • This powerful software suite offers a range of advanced statistical analysis and visualization tools, making it a must-have for any data professional. (alfasoft.com)
  • Statgraphics Centurion is a comprehensive Windows desktop product for statistical analysis, data visualization and predictive analytics. (alfasoft.com)
Models3
  • en]Simply better NIR calibration models. (calibrationmodel.com)
  • Mixture Models, Robustness, and Sum of Squares Proofs. (samuelbhopkins.com)
  • Applying the queuing models for computer systems performance evaluation and/or configurations analysis. (upc.edu)
Linear2
  • Linear and non-linear least squares problems. (gu.se)
  • We also work with the techniques of linear regression and PCA, completing the repertoire of tools for data analysis. (upc.edu)
Comparative1
  • This functionality is concentrated in the phylogenetic analysis of comparative data from species. (phytools.org)
Statistics2
  • Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. (stackexchange.com)
  • A bit of statistics during analysis, and a fair bit of really basic algebra every day. (blogspot.com)
Data analysis2
  • The results confirmed that AMVI can be successfully applied to accurately impute missing values prior to any microarray data analysis. (open.ac.uk)
  • 2021). This package automates initial data analysis and reporting of the results. (warwick.ac.uk)
Results2
  • CTR4 - Capability to manage the acquisition, structuring, analysis and visualization of data and information in the area of informatics engineering, and critically assess the results of this effort. (upc.edu)
  • Especially helpful to practitioners is the StatAdvisor, which explains the results of statistical analyses in a manner suitable for presentation to non-statisticians. (alfasoft.com)