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

Mike Tsionas and Athanasios Andrikopoulos ()
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Athanasios Andrikopoulos: University of Hull

Annals of Operations Research, 2020, vol. 289, issue 2, No 20, 495-501

Abstract: 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.

Keywords: Multimodal data; Uniform mixture model; Regression models; Statistical inference; Bayesian analysis (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10479-019-03475-w

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