Nonparametric analysis of covariance
نویسندگان
چکیده
In the problem of testing the equality of k regression curves from independent samples we discuss three methods using nonparametric estimation techniques of the regression function The rst test is based on a linear combination of estimators for the integrated variance function in the individual samples and in the combined sample The second approach transfers the classical one way analysis of variance to the situation of comparing nonparametric curves while the third test compares the di erences between the estimates of the individual regression functions by means of an L distance We prove asymptotic normality of all considered statistics under the null hypothesis local and xed alternatives with di erent rates corresponding to the various cases Additionally consistency of a wild bootstrap version of the tests is established In contrast to most of the procedures proposed in the literature the methods introduced in this paper are also applicable in the case of di erent design points in each sample and heteroscedastic errors A simulation study is conducted to investigate the nite sample properties of the new tests and a comparison with recently proposed and related procedures is performed AMS Classi cation Primary G
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