BIC selection procedures in mixed effects models - Archive ouverte HAL Access content directly
Reports (Technical Report) Year : 2012

BIC selection procedures in mixed effects models

(1, 2) , (1, 2) , (1, 2)
1
2

Abstract

We consider the problem of variable selection in general nonlinear mixed-e ets models, including mixed-e ects hidden Markov models. These models are used extensively in the study of repeated measurements and longitudinal analysis. We propose a Bayesian Information Criterion (BIC) that is appropriate for nonstandard situations where both the number of subjects N and the number of measurements per subject n tend to in nity. In this case, the consistency rates of the maximum likelihood estimators (MLE) of the parameters depend on the level of variability designed in the model. We show that the MLE of the population parameters related to subject-speci c parameters are \sqrt(N)-consistent whereas the MLE of the parameters related to xed parameters are \sqrt(Nn)-consistent. We derive a BIC criterion with a penalty based on two terms proportional to log(N) and log(Nn). Finite-sample properties of the proposed selection procedure are investigated by simulation studies.
Fichier principal
Vignette du fichier
RR-7948.pdf (780.48 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00696435 , version 1 (11-05-2012)

Identifiers

  • HAL Id : hal-00696435 , version 1
  • PRODINRA : 395976

Cite

Maud Delattre, Marc Lavielle, Marie-Anne Poursat. BIC selection procedures in mixed effects models. [Technical Report] RR-7948, INRIA. 2012. ⟨hal-00696435⟩
472 View
196 Download

Share

Gmail Facebook Twitter LinkedIn More