Bayesian interpretation of Generalized empirical likelihood by maximum entropy

Paul Rochet (1)

1 IMT - Institut de Mathématiques de Toulouse UMR5219

We study a parametric estimation problem related to moment condition models. As an alternative to the generalized empirical likelihood (GEL) and the generalized method of moments (GMM), a Bayesian approach to the problem can be adopted, extending the MEM procedure to parametric moment conditions. We show in particular that a large number of GEL estimators can be interpreted as a maximum entropy solution. Moreover, we provide a more general field of applications by proving the method to be robust to approximate moment conditions.

Mots clés: empirical likelihood; maximum entropy; Bayesian statistics

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Paul Rochet. Bayesian interpretation of Generalized empirical likelihood by maximum entropy. 2011. ⟨hal-00675044⟩