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Activity Number:
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289
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #302740 |
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Title:
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Smoothing Spline Semiparametric Nonlinear Regression Models
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Author(s):
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Yuedong Wang*+ and Chunlei Ke
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Companies:
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University of California, Santa Barbara and Amgen, Inc.
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Address:
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Department of Statistics and Applied Probability, Santa Barbara, CA, 93106,
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Keywords:
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semi-parametric models ; penalized likelihood ; nonlinear functional ; smoothing parameter ; backfitting ; Gauss-Newton algorithm
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Abstract:
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We present a general class of semiparametric nonlinear regression models (SNRM) which assumes that the mean function depends on parameters and nonparametric functions through a known nonlinear functional. SNRMs are natural extensions of both parametric and nonparametric regression models. They include many popular nonparametric and semiparametric models as special cases. We develop a unified estimation procedure based on minimizing penalized likelihood using Gauss-Newton and backfitting algorithms. Smoothing parameters are estimated using the generalized cross-validation and generalized maximum likelihood methods. We derive Bayesian confidence intervals for the unknown functions. A generic and user-friendly R function is developed to implement our estimation and inference procedures. We illustrate our methods with analyses of real data sets.
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