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Activity Number:
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257
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #307665 |
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Title:
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A Goodness-of-Fit Test for Parametric and Semiparametric Models in Multiresponse Regression
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Author(s):
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Ingrid Van Keilegom*+ and Song Xi Chen
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Companies:
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Université catholique de Louvain and Iowa State University
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Address:
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Institute of Statistics, Louvain-la-Neuve, International, 1348, Belgium
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Keywords:
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bootstrap ; empirical likelihood ; goodness-of-fit ; kernel estimation ; monotone regression ; partially linear regression
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Abstract:
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We propose an empirical likelihood test that is able to test the goodness-of-fit of a class of parametric and semiparametric multiresponse regression models. The class includes as special cases fully parametric models, semiparametric models, like the multi-index and the partially linear models, and models with shape constraints. It allows both the response variable and the covariate to 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. The test enjoys good power properties against departures from a hypothesized model within the class.
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