Multiple imputation for continuous variables using a Bayesian principal component analysis

DOI10.1080/00949655.2015.1104683OpenAlexW2100056901MaRDI QIDQ5222464

Vincent Audigier, Julie Josse, François Husson

Publication date: 1 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1401.5747

zbMATH Keywords

multiple imputationdata augmentationmissing valuescontinuous dataBayesian principal component analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Point estimation (62F10) Bayesian inference (62F15) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (8)

An ensemble learning method for variable selection: application to high-dimensional data and missing values ⋮ EPEM: efficient parameter estimation for multiple class monotone missing data ⋮ A Bayesian Singular Value Decomposition Procedure for Missing Data Imputation ⋮ MIDIA: exploring denoising autoencoders for missing data imputation ⋮ A wavelet-based approach for imputation in nonstationary multivariate time series ⋮ Imputation Scores ⋮ Gaussian-based visualization of Gaussian and non-Gaussian-based clustering ⋮ Multiple imputation: a review of practical and theoretical findings

Uses Software

• R
• Amelia
• FactoMineR
• MICE
• clusterGeneration

Cites Work

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