Estimating Markov and Semi-Markov Switching Linear Mixed Models with Individual-Wise Random Effects
Florence Chaubert‐Pereira, Yann Guédon, Christian Lavergne, Catherine Trottier · 2008
Abstract. We address the estimation of Markov (and semi-Markov) switching linear mixed models i.e. models that combine linear mixed models with individual-wise random effects in a (semi-)Markovian manner. A MCEM-like algorithm whose iterations decompose into three steps (sampling of state sequences given random effects, prediction of random effects given the state sequence and maximization) is proposed. This statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks.