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MDL Mean Function Selection in Semiparametric Kernel Regression Models

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Author Info
Jan G. De Gooijer () (University of Amsterdam)
Ao Yuan () (Howard University, Washington DC, USA)
Abstract

We study the problem of selecting the optimal functional form among a set of non-nested nonlinear mean functions for a semiparametric kernel based regression model. To this end we consider Rissanen's minimum description length (MDL) principle. We prove the consistency of the proposed MDL criterion. Its performance is examined via simulated data sets of univariate and bivariate nonlinear regression models.

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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-046/4.

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Date of creation: 07 May 2008
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Handle: RePEc:dgr:uvatin:20080046

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Related research
Keywords: Kernel density estimator Maximum likelihood estimator Minimum description length Nonlinear regression Semiparametric model

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

This paper has been announced in the following NEP Reports:

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  1. Hansen M. H & Yu B., 2001. "Model Selection and the Principle of Minimum Description Length," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 746-774, June. [Downloadable!] (restricted)
  2. Harry Joe, 1989. "Estimation of entropy and other functionals of a multivariate density," Annals of the Institute of Statistical Mathematics, Springer, vol. 41(4), pages 683-697, December. [Downloadable!] (restricted)
  3. Peter Hall & Sally Morton, 1993. "On the estimation of entropy," Annals of the Institute of Statistical Mathematics, Springer, vol. 45(1), pages 69-88, March. [Downloadable!] (restricted)
  4. Ao Yuan & Jan G. De Gooijer, 2007. "Semiparametric Regression with Kernel Error Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association and Swedish Statistical Association, vol. 34(4), pages 841-869. [Downloadable!] (restricted)
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This page was last updated on 2008-7-23.

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