JSM 2004 - Toronto

Abstract #302053

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Activity Number: 341
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #302053
Title: Testing for Covariate Effects in the Fully Nonparametric Analysis of Covariance Model
Author(s): Lan Wang*+ and Michael G. Akritas
Companies: University of Minnesota and Pennsylvania State University
Address: 385 Ford Hall, School of Statistics, Minneapolis, MN, 55455,
Keywords: covairate ; ANCOVA ; nonparametric ; ANOVA
Abstract:

In the context of the fully nonparametric analysis of covariance model of Akritas et al. (2000), we propose methods to test for the presence of covariate main effects and interaction effects between the covariate and factors. The idea underlying the proposed procedures is that covariates can be thought of as factors with many levels. The test statistics are closely related to some recent developments in the asymptotic theory for analysis of variance when the number of factor levels is large. They are very easy to compute and have asymptotic normal laws under the null hypotheses. The methods are investigated on simulated and real data.

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