JSM 2004 - Toronto

Abstract #301277

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Activity Number: 233
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 12:00 PM to 1:50 PM
Sponsor: IMS
Abstract - #301277
Title: Model Selection Based on Maximum Likelihood Estimation: Jackknife Approach
Author(s): Hyunsook Lee*+ and G. Jogesh Babu
Companies: Pennsylvania State University and Pennsylvania State University
Address: 325 Thomas Bldg., University Park, PA, 16802,
Keywords: jackknife ; model selection ; maximum likelihood estimation ; Kullback-Leibler distance ; unbiased estimator
Abstract:

Various model selection methods have been developed to choose a model close to a true model in regression problems, such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Minimum description length (MDL), Mallow C_p, cross-validation, bootstrap information criterion (EIC), corrected Akaike Information Certerion (AICc), unified Akaike Information Criterion (AICu), and minimizing R^2. We present an information criterion for model selection by using jackknife method to minimize Kullback-Leibler distance. The jackknife method reduces bias on Kullback-Leiber distance measure between an unknown true model and an estimated model. We discuss the possibility of using the jackknife information criterion (JIC) as a model selection criterion. The asymtotic property of JIC is studied and Monte Carlo simulations are performed to compare JIC to other information criteria.

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