Principal covariates clusterwise regression (PCCR): accounting for multicollinearity and population heterogeneity in hierarchically organized data

DOI10.1007/s11336-016-9522-0zbMath1360.62537OpenAlexW2558803897WikidataQ31146267 ScholiaQ31146267MaRDI QIDQ525229

Eva Vande Gaer, Tom Frans Wilderjans, Henk A. L. Kiers, Iven van Mechelen, Eva Ceulemans

Publication date: 28 April 2017

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://pure.rug.nl/ws/files/58361026/Principal_Covariates_Clusterwise_Regression_PCCR_.pdf

zbMATH Keywords

multicollinearityclusterwise regressionpopulation heterogeneitycomponent analysishierarchically organized data

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to psychology (62P15)

Related Items (2)

Clusterwise elastic-net regression based on a combined information criterion ⋮ Prediction for regularized clusterwise multiblock regression

Uses Software

• flexmix
• Algorithm 39

Cites Work

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• Simultaneous analysis of coupled data blocks differing in size: a comparison of two weighting schemes
• Correction to: Algorithm 39. Clusterwise linear regression
• Minimization of a class of matrix trace functions by means of refined majorization
• The distribution of chance congruence coefficients from simulated data
• Orthogonal Procrustes rotation for two or more matrices
• Clusterwise linear regression
• A mixture likelihood approach for generalized linear models
• A variable-selection heuristic for K-means clustering
• The local minima problem in hierarchical classes analysis: an evaluation of a simulated annealing algorithm and various multistart procedures
• Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies
• Applied Multiway Data Analysis

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