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Activity Number:
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424
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304052 |
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Title:
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Testing for Linearity of a Semiparametric Generalized Linear Model via Splines
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Author(s):
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Chin-Shang Li*+
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Companies:
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University of California, Davis
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Address:
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Division of Biostatistics, MS1C Rm 145 , Davis, CA, 95616,
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Keywords:
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B-splines ; penalized log-likelihood ratio test
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Abstract:
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The nonparametric component of a semiparametric generalized linear model is modeled by a linear combination of fixed-knot cubic B-splines. A penalized log-likelihood ratio test is proposed to test the null hypothesis of the linearity of the nonparametric function. When the number of knots is fixed and under the null hypothesis, the limiting distribution of the penalized log-likelihood ratio test statistic is the distribution of a linear combination of independent chi-squared random variables, each with one degree of freedom. The smoothing parameter is determined by setting a specified value equal to the asymptotically expected value of the test statistic under the null hypothesis. A simulation study is conducted to evaluate the power performance of the test; a real-life data set is used to illustrate the practical use of the proposed methodology.
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