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

Author

Listed:
  • Mike G. Tsionas

    (Lancaster University Management School)

  • Athanasios Andrikopoulos

    (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.

Suggested Citation

  • Mike G. Tsionas & Athanasios Andrikopoulos, 2020. "A note on the Gao et al. (2019) uniform mixture model in the case of regression," Annals of Operations Research, Springer, vol. 289(2), pages 495-501, June.
    • Handle: RePEc:spr:annopr:v:289:y:2020:i:2:d:10.1007_s10479-019-03475-w
    DOI: 10.1007/s10479-019-03475-w
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