JSM 2004 - Toronto

Abstract #301187

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Activity Number: 107
Type: Topic Contributed
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #301187
Title: Two-stage Nonparametric Approach for Small-area Estimation
Author(s): Pushpal Mukhopadhyay*+ and Tapabrata Maiti
Companies: Iowa State University and Iowa State University
Address: 204, Snedecor Hall, Ames, IA, 50010,
Keywords: nonparametric mixed model ; Nadaraya-Watson kernel estimate ; mean square prediction error
Abstract:

Small-area estimators commonly borrow strength from other related areas. These indirect estimators use models (explicit or implicit) that relate the small areas through supplementary data. Various unit-level and area-level small-area models are proposed in the literature, but all these models assume the small-area mean is linearly related with supplementary information. We propose an area-level nonparametric regression estimator based on Nadaraya-Watson kernel on small-area mean. We adopt a two-stage estimation approach proposed by Prasad and Rao (1990). The asymptotic properties of the proposed estimator are studied and a second order approximation to the mean squared prediction error (MSPE) of the two-stage estimator and the estimator of MSPE approximation are obtained under normality. Finally, we perform a simulation study to show the superiority of the proposed estimator.

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