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Type: Artigo de periódico
Title: Multivariate measurement error models using finite mixtures of skew-Student t distributions
Author: Cabral, CRB
Lachos, VH
Zeller, CB
Abstract: In regression models, the classical assumption of normal distribution of the random observational errors is often violated, masking some important features of the variability present in the data. Some practical actions to solve the problem, like transformation of variables to achieve normality, are often of doubtful utility. In this work we present a proposal to deal with this issue in the context of the simple linear regression model when both the response and the explanatory variable are observed with error. In such models, the experimenter observes a surrogate variable instead of the covariate of interest. We extend the classical normal model by jointly modeling the unobserved covariate and the random errors by a finite mixture of a skewed version of the Student t distribution. This approach allows us to model data with great flexibility, accommodating skewness, heavy tails and multi-modality. We develop a simple EM-type algorithm to perform maximum likelihood inference of the parameters of the proposed model, and compare the efficiency of our method with some competitors through the analysis of some artificial and real data. (C) 2013 Elsevier Inc. All rights reserved.
Subject: Measurement error model
Finite mixtures
EM algorithm
Skew-normal distribution
Skew-Student t distribution
Comparative calibration
Country: EUA
Editor: Elsevier Inc
Citation: Journal Of Multivariate Analysis. Elsevier Inc, v. 124, n. 179, n. 198, 2014.
Rights: fechado
Identifier DOI: 10.1016/j.jmva.2013.10.017
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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