福地 純一郎

The method of selecting the best population by using bootstrap is proposed and its properties are investigated. Its consistency and second-order correctness are proved.

レベニューマネジメントにおける統計的手法をホテル業を例にとって概観する. 特に, ブッキングデータによって将来の予約客室数および販売客室数を予測する方法について, 確率モデルを仮定した上で詳細に論じる. また, 打ち切られた客室需要量データから確率分布のパラメータを推定する方法についても述べる.

In this paper the subsampling method of Carlstein (1986) is used to estimate the risk of prediction for time series data. We extend Carlstein's result by proving strong consistency of the subsampling estimator and we propose a procedure for selecting a time series model empirically from a set of possibly nonnested and misspecified models by using estimated risk of prediction as a selection criterion. When this procedure is applied to the selection of the order of an autoregressive model, it is shown to be a consistent order selector if an appropriate subsample size is chosen. We propose a practical model selection procedure with a common subsample size chosen by Hall & Jing's (1996) procedure.

In this paper, asymptotic properties of bootstrap methods for the maximum of a stationary process are investigated. It is shown that the Efron's bootstrap provides a valid approximation to the sampling distribution of the normalized maximum only in a restricted situation, but the moving block bootstrap is successful in a more general situation. (C) 1999 Elsevier Science B.V. All rights reserved.

We propose methods of constructing confidence intervals for endpoints of a distribution. Under a mild condition on the tail of distribution, asymptotically correct confidence intervals are derived by bootstrapping Weissman's (Comm. Statist. Theory Method A 10 (1981) 549-557) statistics. It is also shown that a modification of this method works for type II censored data.