Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149309 | Journal of Statistical Planning and Inference | 2010 | 14 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
S.H. Hsieh, S.M. Lee, P.S. Shen,