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:	Amsterdam and Rotterdam : Tinbergen Institute
Subject:	Regression \| Maximum-Likelihood-Methode \| Nichtlineares Verfahren \| Nichtparametrisches Verfahren \| Theorie \| Kernel density estimator \| Maximum likelihood estimator \| Minimum description length \| Nonlinear regression \| Semiparametric model

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Series:	Tinbergen Institute Discussion Paper ; 08-046/4
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	83802355X [GVK] hdl:10419/86866 [Handle] RePEc:dgr:uvatin:20080046 [RePEc]
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010325851