Resource title

Nonparametric Analysis of Covariance : the Case of Inhomogeneous and Heteroscedastic Noise

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Resource description

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.

Resource author

Achim Scholz, Natalie Neumeyer, Axel Munk

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Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/22540

Resource license

Adapt according to the presented license agreement and reference the original author.