Afrika Statistika

An Akaike criterion based on Kullback symmetric divergence in the presence of incomplete-data

Bezza Hafidi and Abdallah Mkhadri

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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.

Article information

Source
Afr. Stat., Volume 2, Number 1 (2007), 1-21.

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

Permanent link to this document
https://projecteuclid.org/euclid.as/1495766684

Digital Object Identifier
doi:10.4314/afst.v2i1.46864

Mathematical Reviews number (MathSciNet)
MR2388960

Zentralblatt MATH identifier
1220.62004

Subjects
Primary: 62B10: Information-theoretic topics [See also 94A17]
Secondary: 62-07: Data analysis

Keywords
Akaike information criterion Kullback’s symmetric divergence missing-data model selection EM algorithm regression

Citation

Hafidi, Bezza; Mkhadri, Abdallah. An Akaike criterion based on Kullback symmetric divergence in the presence of incomplete-data. Afr. Stat. 2 (2007), no. 1, 1--21. doi:10.4314/afst.v2i1.46864. https://projecteuclid.org/euclid.as/1495766684


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