Semiparametric Regression with Kernel Error Model

We propose and study a class of regression models, in which the mean function is specified parametricaUy as in the existing regression methods, but the residual distribution is modelled non-parametrically by a kernel estimator, without imposing any assumption on its distribution. This specification...

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Veröffentlicht in:	Scandinavian journal of statistics 2007-12, Vol.34 (4), p.841-869
Hauptverfasser:	YUAN, AO, DE GOOIJER, JAN G.
Format:	Artikel
Sprache:	eng
Schlagworte:	Density estimation Distribution theory Entropy Errors Estimation methods Estimators Exact sciences and technology General topics information bound kernel density estimator Linear inference, regression Mathematics maximum likelihood estimate Maximum likelihood estimation nonlinear regression Parameter estimation Parametric inference Parametric models Probability and statistics Probability theory and stochastic processes Random variables Regression analysis Sciences and techniques of general use semiparametric model Semiparametric modeling Statistical variance Statistics U-statistic Wilks property
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