VARIABLE SELECTION WITH AKAIKE INFORMATION CRITERIA : A COMPARATIVE STUDY

Abstract

In this paper, the problem of variable selection in linear regression is

considered. This problem involves choosing the most appropriate model

from the candidate models. Variable selection criteria based on estimates of

the Kullback-Leibler information are most common. Akaike's AIC and bias

corrected AIC belong to this group of criteria. The reduction of the bias

in estimating the Kullback-Leibler information can lead to better variable

selection. In this study we have compared the Akaike Criterion based on

Fisher Information and AIC criteria based on Kullback-Leibler.

Keywords

• Akaike information criteria,robust selection,Kullback information,variable selection

References

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