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Department of Quantitative Health Sciences/Biostatistics, Cleveland Clinic Foundation, Cleveland, OH 44195, USA.
Multivariate local regression is an important tool for image processing and analysis. In many practical biomedical problems, one is often interested in comparing a group of images or regression surfaces. In this paper, we extend the existing method of testing the equality of nonparametric curves by Dette and Neumeyer (2001) and consider a test statistic by means of an Lgrangian (2)-distance in the multi-dimensional case under a completely heteroscedastic nonparametric model. The test statistic is also extended to be used in the case of spatial correlated errors. Two bootstrap procedures are described in order to approximate the critical values of the test depending on the nature of random errors. The resulting algorithms and analyses are illustrated from both simulation studies and a real medical example.
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