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Measurement Error Models for Replicated Data Under Asymmetric Heavy-Tailed Distributions

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


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© 2017 Springer Science+Business Media New York Replicated data with measurement errors are frequently presented in economical, environmental, chemical, medical and other fields. In this paper, we discuss a replicated measurement error model under the class of scale mixtures of skew-normal distributions, which extends symmetric heavy and light tailed distributions to asymmetric cases. We also consider equation error in the model for displaying the matching degree between the true covariate and response. Explicit iterative expressions of maximum likelihood estimates are provided via the expectation–maximization type algorithm. Empirical Bayes estimates are conducted for predicting the true covariate and response. We study the effectiveness as well as the robustness of the maximum likelihood estimations through two simulation studies. The method is applied to analyze a continuing survey data of food intakes by individuals on diet habits.

Publication metadata

Author(s): Cao C, Wang Y, Shi JQ, Lin J

Publication type: Article

Publication status: Published

Journal: Computational Economics

Year: 2018

Volume: 52

Issue: 2

Pages: 531-553

Print publication date: 01/08/2018

Online publication date: 30/05/2017

Acceptance date: 22/05/2017

ISSN (print): 0927-7099

ISSN (electronic): 1572-9974

Publisher: Springer New York LLC


DOI: 10.1007/s10614-017-9702-8


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