Logistic regression analysis of randomized response data with missing covariates

被引：26

作者：

Hsieh, S. H. ^{[2
]}

Lee, S. M. ^{[1
]}

Shen, P. S. ^{[2
]}

机构：

[1] Feng Chia Univ, Dept Stat, Taichung, Taiwan

[2] Tunghai Univ, Dept Stat, Taichung, Taiwan

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2010年 / 140卷 / 04期

关键词：

Randomized response; Missing at random; Logistic regression; Weighted estimator; 2-STAGE CASE-CONTROL; SEMIPARAMETRIC ESTIMATION; MODEL;

D O I：

10.1016/j.jspi.2009.09.020

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：

Randomized response is an interview technique designed to eliminate response bias when sensitive questions are asked. in this paper, we present a logistic regression model on randomized response data when the covariates on some subjects are missing at random. In particular, we propose Horvitz and Thompson (1952)-type weighted estimators by using different estimates of the selection probabilities. We present large sample theory for the proposed estimators and show that they are more efficient than the estimator using the true selection probabilities. Simulation results support theoretical analysis. We also illustrate the approach using data from a survey of cable TV. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

引用

页码：927 / 940

页数：14

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