Missing Data Imputation Toolbox for MATLAB
作者： Abel Folch-Fortuny , Francisco Arteaga , Alberto Ferrer
DOI: 10.1016/J.CHEMOLAB.2016.03.019
关键词:
摘要: Abstract Here we introduce a graphical user-friendly interface to deal with missing values called Missing Data Imputation (MDI) Toolbox. This MATLAB toolbox allows imputing values, following completely at random patterns, exploiting the relationships among variables. In this way, principal component analysis (PCA) models are fitted iteratively impute data until convergence. Different methods, using PCA internally, included in toolbox: trimmed scores regression (TSR), known (KDR), KDR (KDR-PCR), partial least squares (KDR-PLS), projection model plane (PMP), iterative algorithm (IA), modified nonlinear (NIPALS) and augmentation (DA). MDI Toolbox presents general procedure data, thus can be used infer estimate covariance structure of incomplete matrices, or as preprocessing step other methodologies.
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