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

Tsionas, Mike G.; Andrikopoulos, Athanasios

Abstract

© 2019, The Author(s). We extend the uniform mixture model of Gao et al. (Ann Oper Res, 2019. https://doi.org/10.1007/s10479-019-03236-9) to the case of linear regression. Gao et al. (Ann Oper Res, 2019. https://doi.org/10.1007/s10479-019-03236-9) 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.

Citation

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. https://doi.org/10.1007/s10479-019-03475-w

• 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
• DOI: https://doi.org/10.1007/s10479-019-03475-w
• Keywords: Multimodal data; Uniform mixture model; Regression models; Statistical inference; Bayesian analysis
• Public URL: https://hull-repository.worktribe.com/output/3225300
• Additional Information: First Online: 21 November 2019