Abstract #301643

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JSM 2003 Abstract #301643
Activity Number: 319
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #301643
Title: Semiparametric Regression Technique for Complex Survey Data
Author(s): Zilin Wang*+ and David R. Bellhouse
Companies: University of Western Ontario and University of Western Ontario
Address: Dept. of Statistical & Actuarial Sciences, London, ON, N6G 5B7, Canada
Keywords: complex survey data ; binning ; local polynomial regression ; semiparametric regression
Abstract:

Due to the complexity of the design, survey data are neither independently nor identically distributed. Hence, estimation methods developed for independently and identically distributed data are not suitable for models based on complex survey data.The aim of this paper is to develop an estimation method for a semiparametric regression model for complex survey data. In this semiparametric model, the explanatory variables are represented in two parts: (1) a nonparametric part and (2) a parametric part. The estimating method for this model combines the nonparametric local polynomial regression estimation in complex surveys by Bellhouse and Stafford (1999) and the classic least squares estimation. Moments and the asymptotic properties of the related estimates are derived. Monte Carlo simulations are carried out to examine the properties of the estimates. An empirical illustration is conducted using the 1990 Ontario Health Survey.

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