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

Tsionas, Mike and Andrikopoulos, Athanasios (2020) A note on the Gao et al. (2019) uniform mixture model in the case of regression. Annals of Operations Research, 289. 495–501. ISSN 0254-5330

[thumbnail of paper_REVISED]
Text (paper_REVISED)
paper_REVISED.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (460kB)

Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Annals of Operations Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1800
Subjects:
?? general decision sciencesmanagement science and operations researchdecision sciences(all) ??
ID Code:
139204
Deposited By:
Deposited On:
27 Nov 2019 10:05
Refereed?:
Yes
Published?:
Published
Last Modified:
21 Sep 2024 01:01