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A note on the Gao et al. (2019) uniform mixture model in the case of regression

Tsionas, Mike G.; Andrikopoulos, Athanasios


Mike G. Tsionas


© 2019, The Author(s). We extend the uniform mixture model of Gao et al. (Ann Oper Res, 2019. to the case of linear regression. Gao et al. (Ann Oper Res, 2019. proposed that to characterize the probability distributions of multimodal and irregular data observed in engineering, a uniform mixture model can be used. This model is a weighted combination of multiple uniform distribution components. This case is of empirical interest since, in many instances, the distribution of the error term in a linear regression model cannot be assumed unimodal. Bayesian methods of inference organized around Markov chain Monte Carlo are proposed. In a Monte Carlo experiment, significant efficiency gains are found in comparison to least squares justifying the use of the uniform mixture model.


Tsionas, M. G., & Andrikopoulos, A. (2020). A note on the Gao et al. (2019) uniform mixture model in the case of regression. Annals of Operations Research, 289(2), 495-501.

Journal Article Type Article
Acceptance Date Nov 12, 2019
Online Publication Date Nov 21, 2019
Publication Date Jun 1, 2020
Deposit Date Nov 23, 2019
Publicly Available Date Oct 27, 2022
Journal Annals of Operations Research
Print ISSN 0254-5330
Electronic ISSN 1572-9338
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 289
Issue 2
Pages 495-501
Keywords Multimodal data; Uniform mixture model; Regression models; Statistical inference; Bayesian analysis
Public URL
Additional Information First Online: 21 November 2019


Published article (306 Kb)

Copyright Statement
© The Author(s) 2019. Open Access .This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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