Imputation by PLS regression for linear mixed models

Emilie Guyon (1) , Denys Pommeret (1)

1 IML - Institut de mathématiques de Luminy

The problem of handling missing data for a linear mixed model in presence of correlation between covariates is considered. The missing mechanism concerns both dependent variable and design matrix. We propose an imputation algorithm combining multiple imputation and Partial Least Squares (PLS) analysis methods. Our method relies on two steps: removing random effects, fixed effects are first imputed and PLS components are constructed on the corresponding complete case. The dependent variable is then imputed inside the linear mixed model built by adding the random effects to PLS components. The method is applied on simulations and on real data.

Mots clés: Multiple Imputation; Missing Data; Linear Mixed Regression Model

Domaines: Applications [stat.AP]

Emilie Guyon, Denys Pommeret. Imputation by PLS regression for linear mixed models. 2011. ⟨hal-00582837v2⟩