A new representation method for probability distributions of multimodal and irregular data based on uniform mixture model

Gao, Jianxiong; An, Zongwen; Bai, Xuezong

doi:10.1007/s10479-019-03236-9

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2019

2023

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Cited by 4 publications

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

Tsionas

Andrikopoulos

2019

Ann Oper Res

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We extend the uniform mixture model of Gao et al. ) 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.

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