JSM 2005 Online Program

Abstract #303455

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 - #303455
Title:	On Nonresponse Adjustment via Calibration
Author(s):	Michael Sverchkov*+ and Alan Dorfman and Lawrence R. Ernst and Thomas Moerhle and Steven P. Paben and Chester H. Ponikowski
Companies:	Bureau of Labor Statistics and Bureau of Labor Statistics and Bureau of Labor Statistics and Bureau of Labor Statistics and Bureau of Labor Statistics and Bureau of Labor Statistics
Address:	OSMR BLS Room 1950 PSB, Washington, DC, 20212, United States
Keywords:	calibration ; non-reponse ; regression
Abstract:	We consider estimation of finite population totals in the presence of nonresponse, assuming nonresponses arise randomly within response classes. We compare two regression estimators: one based on the adjusted for nonresponse probability weights, the other based on unadjusted weights. We show that when the auxiliary variables used for nonresponse adjustment are included in the estimators, they differ only slightly. In this case, the nonresponse adjustment step can be omitted from the estimation process without loss of generality (from Result 5 of Deville and Särndal (1992), it follows the same remains correct for a wide class of calibration estimators). We consider a multivariate analog of a "regression through the origin" estimator and show the "adjusted" and "unadjusted" estimators coincide in this case. We also consider calibration estimators under restrictions on weights. We show that if there exists even one set of weights satisfying the calibration equations and restrictions, the benchmark estimator does not depend on the restrictions.

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