Simulation methods for mean and median bias reduction in parametric estimation

被引：9

作者：

Cabrera, J

Watson, GS

机构：

[1] RUTGERS STATE UNIV,DEPT STAT,NEW BRUNSWICK,NJ 08903

[2] PRINCETON UNIV,DEPT MATH,PRINCETON,NJ 08544

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 1997年 / 57卷 / 01期

关键词：

bootstrap; estimating equations;

D O I：

10.1016/S0378-3758(97)81150-0

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The use of the iterated Bootstrap to find estimators that have the correct expectations is now standard. However when the distributions are skewed, or without means, the median makes more sense to us. This paper is primarily concerned with an algorithm that produces estimators whose median equals the unknown parameter. The method is illustrated by its application to four troublesome parametric estimation problems and a dataset.

引用

页码：143 / 152

页数：10

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