LARGE-SAMPLE CONFIDENCE-REGIONS BASED ON SUBSAMPLES UNDER MINIMAL ASSUMPTIONS

• 成果类型: Article
• 署名作者: POLITIS, DN; ROMANO, JP
• 署名单位: Stanford University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176325770
• 发表日期: 1994
• 页码: 2031-2050
• 关键词: mixing sequences; random-variables; bootstrap; jackknife; variance; statistics; VALUES

摘要

In this article, the construction of confidence regions by approximating the sampling distribution of some statistic is studied. The true sampling distribution is estimated by an appropriate normalization of the values of the statistic computed over subsamples of the data. In the i.i.d. context, the method has been studied by Wu in regular situations where the statistic is asymptotically normal. The goal of the present work is to prove the method yields asymptotically valid confidence regions under minimal conditions. Essentially, all that is required is that the statistic, suitably normalized, possesses a limit distribution under the true model. Unlike the bootstrap, the convergence to the limit distribution need not be uniform in any sense. The method is readily adapted to parameters of stationary time series or, more generally, homogeneous random fields. For example, an immediate application is the construction of a confidence interval for the spectral density function of a homogeneous random field.