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2007 An Akaike criterion based on Kullback symmetric divergence in the presence of incomplete-data
Bezza Hafidi, Abdallah Mkhadri
Afr. Stat. 2(1): 1-21 (2007). DOI: 10.4314/afst.v2i1.46864

Abstract

This paper investigates and evaluates an extension of the Akaike information criterion, KIC, which is an approximately unbiased estimator for a risk function based on the Kullback symmetric divergence. KIC is based on the observed-data empirical log-likelihood which may be problematic to compute in the presence of incompletedata. We derive and investigate a variant of KIC criterion for model selection in settings where the observed-data is incomplete. We examine the performance of our criterion relative to other well known criteria in a large simulation study based on bivariate normal model and bivariate regression modeling.

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Bezza Hafidi. Abdallah Mkhadri. "An Akaike criterion based on Kullback symmetric divergence in the presence of incomplete-data." Afr. Stat. 2 (1) 1 - 21, 2007. https://doi.org/10.4314/afst.v2i1.46864

Information

Received: 20 November 2005; Accepted: 10 February 2007; Published: 2007
First available in Project Euclid: 26 May 2017

zbMATH: 1220.62004
MathSciNet: MR2388960
Digital Object Identifier: 10.4314/afst.v2i1.46864

Subjects:
Primary: 62B10
Secondary: 62-07

Rights: Copyright © 2007 The Statistics and Probability African Society

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Vol.2 • No. 1 • 2007
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