Authors: Munk, Axel
Neumeyer, Natalie
Scholz, Achim
Title: Nonparametric Analysis of Covariance - the Case of Inhomogeneous and Heteroscedastic Noise
Language (ISO): en
Abstract: The purpose of this paper is to propose a procedure for testing the equality of several regression curves f_i in nonparametric regression models when the noise is inhomogeneous. This extends work of Dette and Neumeyer (2001) and it is shown that the new test is asymptotically uniformly more powerful. The presented approach is very natural because it transfers the maximum likelihood statistic from a heteroscedastic one way ANOVA to the context of nonparametric regression. The maximum likelihood estimators will be replaced by kernel estimators of the regression functions f_i. It is shown that the asymptotic distribution of the obtained test statistic is nuisance parameter free. Finally, for practical purposes a bootstrap variant is suggested. In a simulation study, level and power of this test will be briefly investigated. In summary, our theoretical findings are supported by this study.
Subject Headings: nonparametric regression
ANOVA
heteroscedasticity
goodness-of-fit
wild bootstrap
efficacy
URI: http://hdl.handle.net/2003/4899
http://dx.doi.org/10.17877/DE290R-15077
Issue Date: 2004
Publisher: Universitätsbibliothek Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

Files in This Item:
File Description SizeFormat 
28_04.pdfDNB235.53 kBAdobe PDFView/Open
tr28-04.ps436.56 kBPostscriptView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.