MDL Mean Function Selection in Semiparametric Kernel Regression Models
AbstractWe 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 InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-046/4.
Date of creation: 07 May 2008
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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 and Methodology: General - - - Semiparametric and Nonparametric Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-06-21 (All new papers)
- NEP-ECM-2008-06-21 (Econometrics)
- NEP-ORE-2008-06-21 (Operations Research)
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