MODERATELY LARGE DEVIATIONS AND EXPANSIONS OF LARGE DEVIATIONS FOR SOME FUNCTIONALS OF WEIGHTED EMPIRICAL PROCESSES

• 成果类型: Article
• 署名作者: INGLOT, T; LEDWINA, T
• 刊物名称: ANNALS OF PROBABILITY
• ISSN/ISSBN: 0091-1798
• DOI: 10.1214/aop/1176989137
• 发表日期: 1993
• 页码: 1691-1705
• 关键词: STATISTICS; probabilities; EFFICIENCY; THEOREMS

摘要

Let alpha(n) be the classical empirical process. Assume T, defined on D[0, 1], satisfies the Lipschitz condition with respect to a weighted sup-norm in D[0, 1]. Explicit bounds for P(T(alpha(n)) greater-than-or-equal-to x(n) square-root n) are obtained for every n greater-than-or-equal-to n0 and all x(n) is-an-element-of (0, sigma], where n0 and sigma are also explicitly given. These bounds lead to moderately large deviations and expansions of the asymptotic large deviations for T(alpha(n)). The present theory closely relates large and moderately large deviations to tails of the asymptotic distributions of considered statistics. It unifies and generalizes some earlier results. In particular, some results of Groeneboom and Shorack are easily derived.