@techreport{Scholz2004Nonparametric,
abstract = {The purpose of this paper is to propose a procedure for testing the equality of several
regression curves fi 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 fi. 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.},
author = {Achim Scholz and Natalie Neumeyer and Axel Munk},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {C52; C14; 330; nonparametric regression; ANOVA; heteroscedasticity; goodness-of-fit; wild bootstrap; efficacy; Nichtparametrisches Verfahren; Regression; Varianzanalyse; Theorie},
language = {eng},
number = {2004,28},
title = {Nonparametric Analysis of Covariance : the Case of Inhomogeneous and Heteroscedastic Noise},
type = {Technical Report / Universit\"{a}t Dortmund, SFB 475 Komplexit\"{a}tsreduktion in Multivariaten Datenstrukturen},
url = {http://hdl.handle.net/10419/22540},
year = {2004}
}
