Local likelihood estimation of truncated regression and its partial derivatives: theory and application

In this paper we propose a very flexible estimator in the context of truncated regression that does not require parametric assumptions. To do this, we adapt the theory of local maximum likelihood estimation. We provide the asymptotic results and illustrate the performance of our estimator on simulated and real data sets. Our estimator performs as good as the fully parametric estimator when the assumptions for the latter hold, but as expected, much better when they do not (provided that the curse of dimensionality problem is not the issue). Overall, our estimator exhibits a fair degree of robustness to various deviations from linearity in the regression equation and also to deviations from the specification of the error term. So the approach shall prove to be very useful in practical applications, where the parametric form of the regression or of the distribution is rarely known.

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Document type	Document de travail (Working Paper)
Access type	Accès libre
Publication date	2006
Language	Anglais
Number of pages	25 pages
Collection	STAT Discussion Papers - 0606
Affiliations	Seoul National University - Department of Statistics UCL - EUEN/STAT - Institut de statistique National University Kyiv -Mohyla Academy - Kyiv Economics Institute
Keywords	Nonparametric Truncated Regression ; Local Likelihood
Links	http://sites.uclouvain.be/IAP-Stat-Phase-V-VI/ISBApub/ISBAdp.html#2006 http://hdl.handle.net/2078.1/122623[Handle]

See also
	[boreal:36290]	Local likelihood estimation of truncated regression and its partial derivatives: Theory and application