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Abstract Details
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Activity Number:
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124
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 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 - #306113 |
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Title:
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Smoothing Goodness-of-Fit tests based on Kullback-Leibler Information
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Author(s):
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Han Yu*+ and Kai-Sheng Song
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Companies:
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Northwest Missouri State University and University of North Texas
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Address:
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1633 N Country Club Road, Maryville, MO, 64468-9762, United States
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Keywords:
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Goodness-of-Fit ;
Distribution free ;
Smoothing
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Abstract:
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We present asymptotically distribution-free goodness-of-fit tests based on smoothing techniques. The proposed tests is a nonparametric extension of the classical Neyman-Pearson log-likelihood ratio test. The tests are indicated to have much greater power for detecting high-frequency nonpara- metric alternatives than the existing classical tests such as Kolmogorov-Smirnov tests. This good performance of the proposed tests is demonstrated by Monte Carlo simulations.
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