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Activity Number:
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417
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #309347 |
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Title:
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Asymptotic Theories in Multiple-Sample Semiparametric Density Ratio Models
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Author(s):
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Guanhua Lu*+ and Benjamin Kedem
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Companies:
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University of Maryland and University of Maryland
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Address:
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Math Dept., 8261 Canning Ter, Greenbelt, MD, 20770,
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Keywords:
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Semiparametric ; Density Ratio Model ; Biased Sampling ; Weak convergence of stochastic processes ; Asymptotic theory ; Gaussian process
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Abstract:
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A multiple-sample semiparametric density ratio model can be constructed by multiplicative exponential distortions of the reference distribution. Distortion functions are assumed to be nonnegative and of a known finite-dimensional parametric form, and the reference distribution is left nonparametric. The combined data from all the samples are used in the large sample problem of estimating each distortion and the reference distribution. The large sample behavior for both the parameters and the unknown reference distribution are studied. The estimated reference distribution is proved to converge weakly to a zero-mean Gaussian process. And the corresponding covariance structure is used to provide the confidence bands. A Kolmogorov-Smirnov type statistic is also studied for a goodness-of-fit test of the density ratio model.
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