Criteria for longitudinal data model selection based on Kullback's symmetric divergence

Hafidi Bezza (1) , Nourddine Azzaoui (2)

1 Chercheur indépendant
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.

Mots clés: Kullback's symmetric divergence; Model selection; AIC; KIC; Longitudinal data analysis; Kullback's symmetric divergence.

Domaines: Théorie [stat.TH]; Applications [stat.AP]; Méthodologie [stat.ME]

Hafidi Bezza, Nourddine Azzaoui. Criteria for longitudinal data model selection based on Kullback's symmetric divergence. Revue Africaine de Recherche en Informatique et Mathématiques Appliquées, 2012, 15, pp 83--99. ⟨hal-00732879⟩