Using generalized Procrustes analysis for multiple imputation in principal component analysis

DOI10.1007/s00357-014-9154-yzbMath1360.62307OpenAlexW2069087665MaRDI QIDQ288990

Joost R. van Ginkel, Pieter M. Kroonenberg

Publication date: 27 May 2016

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-014-9154-y

zbMATH Keywords

principal component analysismissing datamultiple imputationconvex hullsgeneralized Procrustes analysisquestionnaires

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to psychology (62P15)

Related Items (5)

Evaluation of multiple-imputation procedures for three-mode component models ⋮ GPAbin: unifying visualizations of multiple imputations for missing values ⋮ Comparisons among several methods for handling missing data in principal component analysis (PCA) ⋮ Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling ⋮ Missing data in principal component analysis of questionnaire data: a comparison of methods

Uses Software

• S-PLUS
• SPSS
• SAS
• mi

Cites Work

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• SAS
• Concise formulas for the standard errors of component loading estimates
• Two-way imputation: a Bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximation
• Generalized Procrustes analysis
• Orthogonal Procrustes rotation for two or more matrices
• A direct approach to individual differences scaling using increasingly complex transformations
• Weighted least squares fitting using ordinary least squares algorithms
• Multiple imputation in principal component analysis
• Standard errors for rotated factor loadings
• Principal component analysis.
• The orthogonal approximation of an oblique structure in factor analysis
• Fully conditional specification in multivariate imputation
• Annotated bibliography of three-mode factor analysis
• Inference and missing data
• Constructing bootstrap confidence intervals for principal component loadings in the presence of missing data: A multiple‐imputation approach
• Relationships Between two Methods for Dealing with Missing Data in Principal Component Analysis
• Application of the Parametric Bootstrap to Models that Incorporate a Singular Value Decomposition
• Applied Multiway Data Analysis
• Asymptotic Theory for Principal Component Analysis

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