%0 Unpublished work
%T Imputation by PLS regression for generalized linear mixed models
%+ Institut de mathématiques de Luminy (IML)
%A Guyon, Emilie
%A Pommeret, Denys
%8 2011-12-08
%D 2011
%K Missing data
%K Multiple imputation
%K PLS regression
%K Schall linearization
%K Generalized linear mixed models
%Z Mathematics [math]/Statistics [math.ST]
%Z Statistics [stat]/Statistics Theory [stat.TH]Preprints, Working Papers, ...
%X The problem of handling missing data in generalized linear mixed models with correlated covariates is considered when the missing mechanism concerns both the response variable and the covariates. An imputation algorithm combining multiple imputation and Partial Least Squares (PLS) regression is proposed. The method relies on two steps. In a first step, using a linearization technique, the generalized linear mixed model is approximated by a linear mixed model. A latent variable is introduced and its associated PLS components are constructed. In a second step these PLS components are used in the generalized linear mixed model to impute the response variable. The method is applied on simulations and on a real data.
%G English
%2 https://hal.science/hal-00650295/document
%2 https://hal.science/hal-00650295/file/MI_PLS_GL2M_GuyonPommeret.pdf
%L hal-00650295
%U https://hal.science/hal-00650295
%~ CNRS
%~ UNIV-AMU
%~ IML
%~ I2M