Robust errors-in-variables linear regression via Laplace distribution

Robust estimation procedures for linear and mixture linear errors-in-variables regression models are proposed based on the relationship between the least absolute deviation criterion and maximum likelihood estimation in a Laplace distribution. The finite sample performance of the proposed procedures is evaluated by simulation studies.

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Year of publication:	2014
Authors:	Shi, Jianhong ; Chen, Kun ; Song, Weixing
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 84.2014, C, p. 113-120
Publisher:	Elsevier
Subject:	Least absolute deviation \| EM algorithm \| Mixture regression model \| Normal mixture \| Laplace distribution

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

Persistent link: https://www.econbiz.de/10010718799