JSM 2004 - Toronto

Abstract #300086

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Activity Number: 2
Type: Invited
Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor: WNAR
Abstract - #300086
Title: Functional Regression for Longitudinal Data
Author(s): Hans-Georg Mueller*+
Companies: University of California, Davis
Address: Department of Statistics, Davis, CA, 95616,
Keywords: functional data analysis ; sparse data ; smoothing
Abstract:

We review functional regression models with emphasis on situations where the response is a random trajectory. In longitudinal data analysis, observation times of trajectories are typically irregular and sparse. We present nonparametric methods that are based on several local least squares smoothing steps and can handle such sparse data. By borrowing strength from the entire sample of observed curves, nonparametric estimation becomes feasible in situations with sparse data that are normally not conducive to applications of smoothing. In addition to asymptotic results including rates of convergence, we discuss the concept of a residual process that replaces the ordinary residuals in classical regression and is useful in studying goodness of fit for functional regression. The methods are illustrated with examples from biomedical studies. This presentation is based on joint work with Jeng-Min Chiou, Peter Hall, Jane-Ling Wang and Fang Yao.

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Revised March 2004