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Heteroscedastic replicated measurement error models under asymmetric heavy-tailed distributions

Lookup NU author(s): Dr Chunzheng Cao, Dr Jian Shi

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Abstract

© 2017 Springer-Verlag Berlin Heidelberg We propose a heteroscedastic replicated measurement error model based on the class of scale mixtures of skew-normal distributions, which allows the variances of measurement errors to vary across subjects. We develop EM algorithms to calculate maximum likelihood estimates for the model with or without equation error. An empirical Bayes approach is applied to estimate the true covariate and predict the response. Simulation studies show that the proposed models can provide reliable results and the inference is not unduly affected by outliers and distribution misspecification. The method has also been used to analyze a real data of plant root decomposition.


Publication metadata

Author(s): Cao C, Chen M, Wang Y, Shi JQ

Publication type: Article

Publication status: Published

Journal: Computational Statistics

Year: 2018

Volume: 33

Issue: 1

Pages: 319-338

Print publication date: 01/03/2018

Online publication date: 08/03/2017

Acceptance date: 27/02/2017

ISSN (print): 0943-4062

ISSN (electronic): 1613-9658

Publisher: Springer Verlag

URL: https://doi.org/10.1007/s00180-017-0720-8

DOI: 10.1007/s00180-017-0720-8


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