JSM 2004 - Toronto

Abstract #300954

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Activity Number: 113
Type: Contributed
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #300954
Title: Profile-kernel vs. Backfitting in the Estimation of Partially Linear Models
Author(s): Zonghui Hu*+ and Naisyin Wang and Raymond J. Carroll
Companies: Texas A&M University and Texas A&M University and Texas A&M University
Address: 1 Hensel Dr., #W3K, College Station, TX, 77840,
Keywords: local linear smoothing ; backfitting ; bandwidth ; consistency ; asymptotics
Abstract:

We study the profile-kernel and backfitting method in partially linear models for clustered/longitudinal data. For independent data, despite the inconsistency of backfitting estimator noted by Rice (1986), the two estimators have the same asymptotic variance as shown by Opsomer and Ruppert (1999). When an undersmoothing nonparametric procedure is adopted, the two methods are considered as equivalent. Theoretical comparisons of the two estimators for multivariate responses are investigated. We find out that for correlated data, backfitting often produces a larger asymptotic variance than the profile-kernel, in addition to its bias problem. Our simulation study clearly shows that performance of profile-kernel is superior to that of backfitting for finite samples. The application of both methods to an opthalmology dataset is provided.

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