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Abstract Details
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Activity Number:
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29
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 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 - #305674 |
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Title:
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Model Selection and Estimation in Generalized Additive Models
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Author(s):
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Dong Wang*+ and Daowen Zhang
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Companies:
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North Carolina State University and North Carolina State University
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Address:
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Department of Statistics, Raleigh, NC, 27695-8203, United States
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Keywords:
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Adaptive LASSO ;
EM Algorithm ;
Smoothing Spline Estimators ;
Variable Selection ;
Nonparametric Function
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Abstract:
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A method of model selection and estimation in generalized additive models (GAMs) is proposed. The generalized linear mixed model representation is constructed by utilizing the linear mixed model representation of the smoothing spline estimators of the nonparametric functions, where the importance of the functions is controlled by treating the inverse of the smoothing parameters as extra variance components. By maximizing the penalized quasi-likelihood with the adaptive LASSO, we could effectively select the important nonparametric functions. A unified EM algorithm is also provided to obtain both the maximum likelihood and maximum penalized likelihood estimate of the variance components induced from nonparametric functions. In addition, we estimate the selected functions by the best linear unbiased predictors (BLUPs).
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