Mike G. Tsionas
A note on the Gao et al. (2019) uniform mixture model in the case of regression
Tsionas, Mike G.; Andrikopoulos, Athanasios
Authors
Dr Thanos Andrikopoulos A.Andrikopoulos@hull.ac.uk
Senior Lecturer (Associate Professor) in Finance
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 |
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Copyright Statement
© The Author(s) 2019. Open Access .This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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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