Bootstrap Estimate of Kullback-leibler Information for Model Selection Bootstrap Estimate of Kullback-leibler Information for Model Selection

Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical model selection procedure which is based on likelihood principle like AIC. To discriminate nested models, we have to estimate it up to the order of constant while the Kullback-Leibler information itself is of the order of the number of observations. A correction term employed in AIC is an example to ful ll this requirement but it is a simple minded bias correction to the log maximum likelihood. Therefore there is no assurance that such a bias correction yields a good estimate of Kullback-Leibler information. In this paper as an alternative, bootstrap type estimation is considered. We will rst show that both bootstrap estimates proposed by Efron (1983,1986,1993) and Cavanaugh and Shumway(1994) are at least asymptotically equivalent and there exist many other equivalent bootstrap estimates. We also show that all such methods are asymptotically equivalent to a non-bootstrap method, known as TIC ( Takeuchi's Information Criterion) which is a generalization of AIC.