Lookup NU author(s): Dr Chunzheng Cao,
Dr Jian Shi
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© 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.
Author(s): Cao C, Chen M, Wang Y, Shi JQ
Publication type: Article
Publication status: Published
Journal: Computational Statistics
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
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