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

Bezza Hafidi 1 Nourddine Azzaoui 2 
2 Probabilité, Analyse et Statistiques
LMBP - Laboratoire de Mathématiques Blaise Pascal
Abstract : 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
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Bezza Hafidi, Nourddine Azzaoui. Criteria for longitudinal data model selection based on Kullback’s symmetric divergence. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2012, Volume 15, 2012, pp.83-99. ⟨10.46298/arima.1959⟩. ⟨hal-01299492⟩



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