Abstract #300875

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JSM 2003 Abstract #300875
Activity Number: 418
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #300875
Title: Testing Hypotheses in Nonparametric Mixed-Effects Model when the Number of Repeated Measurements Is Large
Author(s): Haiyan Wang*+ and Michael G. Akritas
Companies: Pennsylvania State University and Pennsylvania State University
Address: 200 North Danielle Dr., Bellefonte, PA, 16823,
Keywords: testing hypotheses ; mixed-effects model ; longitudinal data analysis ; nonparametric approach ; projection method
Abstract:

Linear mixed-effects models are used commonly to analyze repeated measurements taken over time on each individual in one or more treatment groups. By specifying different parameters and covariance structures, linear mixed-effects models can accommodate multivariate analysis of variance, univariate analysis, time series models and covariate effects. However, the choice of covariance structures is a concern. To release the dependence on the covariance structure, nonlinear mixed-effects models and other nonparametric procedures are investigated by many researchers. We use a purely nonparametric univariate model with no distribution assumption other than the fact that the dependence between the observations from the same subject gets weaker as they are far apart in time and give the asymptotic results on the test of the interaction effect, main time effect, main group effect, and simple group effect. A projection method suggested by Akritas and Papadatos (2003) is used to derive the asymptotic distribution of the F statistics. Our simulation study confirmed the proposed testing procedures. The method can also be extended to the test of covariate effects.

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