A note on reducing the bias of the approximate Bayesian bootstrap imputation variance estimator

被引：8

作者：

Parzen, M ^{[1
]}

Lipsitz, SR

Fitzmaurice, GM

机构：

[1] Emory Univ, Goizueta Business Sch, Atlanta, GA 30322 USA

[2] Brigham & Womens Hosp, Div Gen Med, Boston, MA 02120 USA

来源：

BIOMETRIKA | 2005年 / 92卷 / 04期

基金：

美国国家卫生研究院;

关键词：

missing data; relative bias; variance estimation;

D O I：

10.1093/biomet/92.4.971

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Rubin & Schenker (1986) proposed the approximate Bayesian bootstrap, a two-stage resampling procedure, as a method of creating multiple imputations when missing data are ignorable. Kim (2002) showed that the multiple imputation variance estimator is biased for moderate sample sizes when this method is used. To reduce the bias, Kim (2002) proposed modifying the number of samples drawn at the first stage of the Bayesian bootstrap procedure. In this note, we suggest an alternative method for reducing the bias via a simple correction factor applied to the standard multiple imputation variance estimate. The proposed correction is more easily implemented and more efficient than the procedure proposed by Kim (2002).

引用

页码：971 / 974

页数：4