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• W3100183954 abstract "We propose an empirical likelihood test that is able to test the goodness of fit of a class of parametric and semi-parametric multiresponse regression models. The class includes as special cases fully parametric models; semi-parametric models, like the multiindex and the partially linear models; and models with shape constraints. Another feature of the test is that it allows both the response variable and the covariate be multivariate, which means that multiple regression curves can be tested simultaneously. The test also allows the presence of infinite-dimensional nuisance functions in the model to be tested. It is shown that the empirical likelihood test statistic is asymptotically normally distributed under certain mild conditions and permits a wild bootstrap calibration. Despite the large size of the class of models to be considered, the empirical likelihood test enjoys good power properties against departures from a hypothesized model within the class." @default.
• W3100183954 created "2020-11-23" @default.
• W3100183954 creator A5006321255 @default.
• W3100183954 creator A5025169649 @default.
• W3100183954 date "2009-11-01" @default.
• W3100183954 modified "2023-10-16" @default.
• W3100183954 title "A goodness-of-fit test for parametric and semi-parametric models in multiresponse regression" @default.
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• W3100183954 doi "https://doi.org/10.3150/09-bej208" @default.
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