Estimation of Linear Error-in-Covariables Models with Validation Data Under Random Censorship

Consider the linear models of the form Y=X[tau][beta]+[var epsilon] with the response Y censored randomly on the right and X measured erroneously. Without specifying any error models, in this paper, a semiparametric method is applied to the estimation of the parametric vector [beta] with the help of proper validation data. For the proposed estimator, an asymptotic representation is established and the asymptotic normality is also proved.

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Year of publication:	2000
Authors:	Wang, Qihua
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 74.2000, 2, p. 245-266
Publisher:	Elsevier
Subject:	linear model \| validation data \| random censoring \| asymptotic normality

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

Persistent link: https://www.econbiz.de/10005199570