Bezza Hafidi ; Nourddine Azzaoui - Criteria for longitudinal data model selection based on Kullback’s symmetric divergence

arima:1959 - Revue Africaine de Recherche en Informatique et Mathématiques Appliquées, November 21, 2012, Volume 15, 2012 - https://doi.org/10.46298/arima.1959
Criteria for longitudinal data model selection based on Kullback’s symmetric divergenceArticle

Authors: Bezza Hafidi 1; Nourddine Azzaoui ORCID2

  • 1 Université Inb Zohr
  • 2 Probabilité, Analyse et Statistiques

Recently, Azari et al (2006) showed that (AIC) criterion and its corrected versions cannot be directly applied to model selection for longitudinal data with correlated errors. They proposed two model selection criteria, AICc and RICc, by applying likelihood and residual likelihood approaches. These two criteria are estimators of the Kullback-Leibler's divergence distance which is asymmetric. In this work, we apply the likelihood and residual likelihood approaches to propose two new criteria, suitable for small samples longitudinal data, based on the Kullback's symmetric divergence. Their performance relative to others criteria is examined in a large simulation study


Volume: Volume 15, 2012
Published on: November 21, 2012
Submitted on: May 21, 2012
Keywords: Model selection, AIC, KIC, Longitudinal data analysis, Kullback's symmetric divergence.,[INFO] Computer Science [cs],[MATH] Mathematics [math]

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