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

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A note on the Gao et al. (2019) uniform mixture model in the case of regression. / Tsionas, Mike; Andrikopoulos, Athanasios.
In: Annals of Operations Research, Vol. 289, 01.06.2020, p. 495–501.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, M & Andrikopoulos, A 2020, 'A note on the Gao et al. (2019) uniform mixture model in the case of regression', Annals of Operations Research, vol. 289, pp. 495–501. https://doi.org/10.1007/s10479-019-03475-w

APA

Tsionas, M., & Andrikopoulos, A. (2020). A note on the Gao et al. (2019) uniform mixture model in the case of regression. Annals of Operations Research, 289, 495–501. https://doi.org/10.1007/s10479-019-03475-w

Vancouver

Tsionas M, Andrikopoulos A. A note on the Gao et al. (2019) uniform mixture model in the case of regression. Annals of Operations Research. 2020 Jun 1;289:495–501. Epub 2019 Nov 21. doi: 10.1007/s10479-019-03475-w

Author

Tsionas, Mike ; Andrikopoulos, Athanasios. / A note on the Gao et al. (2019) uniform mixture model in the case of regression. In: Annals of Operations Research. 2020 ; Vol. 289. pp. 495–501.

Bibtex

@article{def9318296294511b30cfac3fb09c430,
title = "A note on the Gao et al. (2019) uniform mixture model in the case of regression",
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.",
author = "Mike Tsionas and Athanasios Andrikopoulos",
year = "2020",
month = jun,
day = "1",
doi = "10.1007/s10479-019-03475-w",
language = "English",
volume = "289",
pages = "495–501",
journal = "Annals of Operations Research",
issn = "0254-5330",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - A note on the Gao et al. (2019) uniform mixture model in the case of regression

AU - Tsionas, Mike

AU - Andrikopoulos, Athanasios

PY - 2020/6/1

Y1 - 2020/6/1

N2 - 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.

AB - 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.

U2 - 10.1007/s10479-019-03475-w

DO - 10.1007/s10479-019-03475-w

M3 - Journal article

VL - 289

SP - 495

EP - 501

JO - Annals of Operations Research

JF - Annals of Operations Research

SN - 0254-5330

ER -