JSM 2005 Online Program

Abstract #304316

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	445
Type:	Topic Contributed
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Survey Research Methods
Abstract - #304316
Title:	Calibration Estimation Using Empirical Likelihood Ratio in Survey
Author(s):	Jae-kwang Kim*+ and Tae Hoon Lee
Companies:	Yonsei University and Yonsei University
Address:	Department of Applied Statistics, Seoul, 120-749, South Korea
Keywords:	regression estimation ; weighting ; auxiliary information ; optimal estimation ; nonparametric
Abstract:	Calibration estimation, which can be roughly described as a method of adjusting the original design weights to incorporate the known population totals of the auxiliary variables, has become popular in sample surveys. The calibration weights are chosen to minimize a given distance measure while satisfying a set of constraints related with the auxiliary variable information. Under the simple random sampling, Chen and Qin (1993) suggested that the calibration estimator that maximizes the empirical likelihood can make efficient use of the auxiliary variables. We propose a new calibration procedure that uses the empirical likelihood ratio as a distance function. The resulting calibration estimator can be extended easily to the unequal probability selection sampling. Asymptotic properties of the proposed calibration estimator are discussed. A consistent variance estimator also is proposed and the results from a limited simulation study are presented.

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