Likelihood-based kernel estimation in semiparametric errors-in-covariables models with validation data

We present methods to handle error-in-variables models. Kernel-based likelihood score estimating equation methods are developed for estimating conditional density parameters. In particular, a semiparametric likelihood method is proposed for sufficiently using the information in the data. The asymptotic distribution theory is derived. Small sample simulations and a real data set are used to illustrate the proposed estimation methods.

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Year of publication:	2007
Authors:	Wang, Qihua ; Yu, Keming
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 98.2007, 3, p. 455-480
Publisher:	Elsevier
Keywords:	Conditional density estimation Empirical likelihood Kernel estimation Measurement error Surrogate variables

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005221212