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Activity Number: 452
Type: Invited
Date/Time: Wednesday, August 9, 2006 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #305079
Title: Variance Estimation for Empirical Likelihood Calibration Estimators in Unequal Probability Sampling
Author(s): Jae-kwang Kim*+
Companies: Yonsei University
Address: Department of Applied Statistics, Seoul, 120-749, Korea
Keywords: survey sampling ; weighting ; benchmarking ; nonparametric maximum likelihood estimator
Abstract:

Empirical likelihood calibration estimation uses an empirical likelihood function to adjust design weights to fit calibration constraints. Under unequal probability sampling, two types of the empirical likelihood function have been considered. Chen and Sitter (1999) proposed using the pseudo-empirical likelihood function for calibration. Recently, Kim (2005) proposed using another type of empirical likelihood function for calibration estimation. In this paper, we compare the two empirical likelihood calibration estimators in detail and discuss the situations where one method is preferable to the other. Variance estimator using a modification of Binder (1983) linearization method is proposed. The proposed method is shown to be robust against the failure of the homogeneous design weight assumptions. Results from a limited simulation study also are presented.

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