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:	2008-05-07
Authors:	Gooijer, Jan G. De ; Yuan, Ao
Institutions:	Tinbergen Institute
Subject:	Kernel density estimator \| Maximum likelihood estimator \| Minimum description length \| Nonlinear regression \| Semiparametric model

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Extent:	application/pdf
Series:	Tinbergen Institute Discussion Papers.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series Tinbergen Institute Discussion Papers Number 08-046/4
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005137347