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MDL Mean Function Selection in Semiparametric Kernel Regression Models


Author Info

  • Jan G. De Gooijer

    (University of Amsterdam)

  • Ao Yuan

    (Howard University, Washington DC, USA)


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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Bibliographic Info

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

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  1. 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.
  2. Ao Yuan & Jan G. De Gooijer, 2006. "Semiparametric Regression with Kernel Error Model," Tinbergen Institute Discussion Papers 06-058/4, Tinbergen Institute.
  3. Hall, Peter, 1986. "On powerful distributional tests based on sample spacings," Journal of Multivariate Analysis, Elsevier, vol. 19(2), pages 201-224, August.
  4. 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.
  5. 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.
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