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Activity Number: 277
Type: Contributed
Date/Time: Tuesday, August 4, 2009 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #303611
Title: Analysis of Longitudinal Data Using Partial Linear Models with Quadratic Inference Functions
Author(s): Wing K. Fung*+
Companies: The University of Hong Kong
Address: Pokfulam Road, Hong Kong, 0, China
Keywords: B-spline ; longitudinal data ; partial linear model ; quadratic inference functions
Abstract:

We consider improved estimating equations for semiparametric partial linear models (PLM) for longitudinal data, or clustered data in general. We approximate the nonparametric function in the PLM by a regression spline, and utilize quadratic inference functions (QIF) in the estimating equations to achieve a more efficient estimation of the parametric part in the model, even when the correlation structure is misspecified. Moreover, we construct a test which is an analogue to the likelihood ratio inference function for inferring the parametric component in the model. The proposed methods perform well in simulation studies and real data analysis.

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