MDL Mean Function Selection in Semiparametric Kernel Regression Models

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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Year of publication:	2008
Authors:	Gooijer, Jan G. de ; Yuan, Ao
Publisher:	[S.l.] : SSRN
Subject:	Regressionsanalyse \| Regression analysis \| Theorie \| Theory \| Nichtparametrisches Verfahren \| Nonparametric statistics \| Nichtlineare Regression \| Nonlinear regression \| Maximum-Likelihood-Schätzung \| Maximum likelihood estimation

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