We propose a two-stage model selection procedure for the linear mixed-effects models. The procedure consists of two steps: First, penalized restricted log-likelihood is used to select the random effects, and this is done by adopting a Newton-type algorithm. Next, the penalized log-likelihood is used to select the fixed effects via pathwise coordinate optimization to improve the computation efficiency. We prove that our procedure has the oracle properties. Both simulation studies and a real data example are carried out to examine finite sample performance of the proposed fixed and random effects selection procedure. Supplementary materials including R code used in this article and proofs for the theorems are available online.

No abstract

No abstract

No abstract

Abstract:The authors consider general estimators for the mean and variance parameters in the random effect model and in the transformation model for data with multiple levels of variation. They show that these estimators have different distributions under the two models unless all the variables have Gaussian distributions. They investigate the asymptotic properties of bootstrap procedures designed for the two models. They also report simulation results and illustrate the bootstraps using data on red spruce trees. Rééchantillonnage de donnéesà variation multiniveauRésumé : Les auteurs s'intéressentà des estimateurs généraux des paramètres de moyenne et variance dans le modèleà effets aléatoires et le modèle de transformation pour des donnéesà variation multiniveau. Ils montrent que la loi de ces estimateurs dépend du modèle sauf si toutes les variables sont gaussiennes. Ils explorent les propriétés asymptotiques de procédures bootstrap propres aux deux modèles. Ils présentent des résultats de simulation et illustrent l'emploi de ces bootstrapsà l'aide de données sur l'épinette rouge.