Minimaxity in Estimation of Restricted Parameters

T. Kubokawa
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引用次数: 29

Abstract

This paper is concerned with estimation of the restricted parameters in location and/or scale families from a decision-theoretic point of view. A simple method is provided to show the minimaxity of the best equivariant and unrestricted estimators. This is based on a modification of the known method of Girshick and Savage (1951) and can be applied to more complicated cases of restriction in the location-scale family. Classes of minimax estimators are also constructed by using the IERD method of Kubokawa (1994a, b): Especially, the paper succeeds in constructing such a class for estimating a restricted mean in a normal distribution with an unknown variance.
受限参数估计中的极小性
本文从决策理论的角度研究了位置族和/或尺度族中受限参数的估计问题。给出了一种简单的方法来证明最佳等变估计量和无限制估计量的极小性。这是基于对Girshick和Savage(1951)已知方法的修改,可以应用于位置尺度家族中更复杂的限制情况。Kubokawa (1994a, b)的IERD方法也构造了极大极小估计量的类:特别是,本文成功地构造了一个用于估计方差未知的正态分布中的限制均值的类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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