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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

Authors

Mike G. Tsionas

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Dr Thanos Andrikopoulos A.Andrikopoulos@hull.ac.uk
Lecturer in Finance & Programme Director BSc Financial Management at University of Hull



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.

Journal Article Type Article
Journal Annals of Operations Research
Print ISSN 0254-5330
Electronic ISSN 1572-9338
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
APA6 Citation Tsionas, M. G., & Andrikopoulos, A. (in press). A note on the Gao et al. (2019) uniform mixture model in the case of regression. Annals of Operations Research, https://doi.org/10.1007/s10479-019-03475-w
DOI https://doi.org/10.1007/s10479-019-03475-w
Keywords Management Science and Operations Research; General Decision Sciences; Multimodal data; Uniform mixture model; Regression models; Statistical inference; Bayesian analysis
Additional Information First Online: 21 November 2019

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