Statistical Aspects of Model Selection

Shibata, R. (1989). Statistical Aspects of Model Selection. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-89-077

Preview

Text WP-89-077.pdf Download (980kB) | Preview

Abstract

Various aspects of statistical model selection are discussed from the view point of a statistician. Our concern here is about selection procedures based on the Kullback Leibler information number. Derivation of AIC (Akaike's Information Criterion) is given. As a result a natural extension of AIC, called TIC (Takeuchi's Information Criterion) follows. It is shown that the TIC is asymptotically equivalent to Cross Validation in a general context, although AIC is asymptotically equivalent only for the case of independent identically distributed observations. Next, the maximum penalized likelihood estimate is considered in place of the maximum likelihood estimate as an estimation of parameters after a model is selected. Then the weight of penalty is also the one to be selected. We will show that, starting from the same Kullback-Leibler information number, a useful criterion RIC (Regularization Information Criterion) is derived to select both the model and the weight of penalty. This criterion is in fact an extension of TIC as well, as of AIC. Comparison of various criteria, including consistency and efficiency is summarized in Section 5. Applications of such criteria to time series models are given in the last section.

Actions (login required)

View Item