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Activity Number:
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33
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #306458 |
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Title:
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A Goodness-of-Fit Test for Parametric Regression Models When Some Covariates are Missing
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Author(s):
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Lei Jin*+ and Suojin Wang
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Companies:
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Texas A&M University and Texas A&M University
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Address:
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1100 Hensel Drive, Apt. T1L, College Station, TX, 77840,
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Keywords:
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goodness-of-fit ; missing data ; nonparametric
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Abstract:
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Several methods have been proposed for testing adequacy of parametric models against nonparametric alternatives. These methods may encounter difficulties when observed data are partially missing. In this paper, we propose a test for linear models when some covariates are partially missing. We investigate its asymptotic properties in comparison to existing methods. Simulation studies are also given to demonstrate the finite sample performances of these methods.
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