Abstract #302172

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JSM 2003 Abstract #302172
Activity Number: 413
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #302172
Title: A Simulation Study to Compare Unit-Level Nonlinear Mixed Model Methods for Small Area Estimation for Survey Data
Author(s): Akhil K. Vaish*+ and Avinash C. Singh and Ralph E. Folsom
Companies: Research Triangle Institute and RTI International and Research Triangle Institute
Address: PO Box 12194, Rtp, NC, 27709-2194,
Keywords: estimating functions ; pseudo score functions ; survey weighted HB ; MCMC
Abstract:

The problem of fitting nonlinear mixed models to survey data using unit-level covariate information for producing small area estimation arose in the context of the National Survey on Drug Use and Health (formerly known as the National Household Survey on Drug Abuse). This task was addressed by Folsom, Shah, and Vaish (1999) and Shah et al. (2000). An improved method was developed by Singh, Folsom, and Vaish (2002), which is based on Estimating Function Likelihood and it takes full account of the survey design. In this paper, we conduct a simulation study to compare the above two methods with the empirical Bayes method of Fay and Herriot (1979). The methods are compared by a simple simulation study where a HB nonlinear mixed model is fitted to the data obtained from a nonignorable sample design. Both fixed and random parameters are estimated to construct small area estimates. MCMC is used for HB parameter estimation.

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