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Semiparametric Model Selection With Applications To Regression  (Make Corrections)  
Zhanlue Zhao Huimin Chen X. Rong Li Department of Electrical Engineering...



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Abstract: In this paper we consider model selection problem using samples of small or moderate size where each model can have unknown parameter without a fully specified likelihood function. A semiparametric model selection criterion is proposed where the penalty-based model complexity term is used for the parameter with fully specified model structure and the kernel density estimation is used for the unknown noise distribution. A linear regression problem with various noise distributions is studied and... (Update)

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BibTeX entry:   (Update)

@misc{ huimin-semiparametric,
  author = "Zhanlue Zhao Huimin",
  title = "Semiparametric Model Selection With Applications To Regression",
  url = "citeseer.ist.psu.edu/763469.html" }
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82   The Minimum Description Length Principle in Coding and Model.. (context) - Barron, Rissanen et al. - 1998
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3   Stochastic Complexity and the MDL Principle (context) - Rissanen - 1987
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1   Conditional Model Order Estimation (context) - Kay - 1910
1   Boosting a Weaking Learning Algorithm by Majority (context) - Freund - 1996
1   Kernel Probability Density Estimation Methods (context) - Towers - 2002
1   A Comparative Study on Model Selection and Multiple Model Fu.. (context) - Chen, Huang - 2005

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