基于非对称拉普拉斯分布的混合分位数回归参数估计

doi:10.12066/j.issn.1007-2861.2125

Abstract

Abstract:

A new mixed quantile regression model is established using an asymmetric Laplace distribution. Traditional models consider only positional parameters, whereas our model considers the regression of both positional and scale parameters. The expectation maximization (EM) algorithm was used to compute the estimated values of the model parameters. Numerical simulation results showed that the proposed parameter estimation was precise in each quantile, and a larger sample offered higher precision. Our model was applied to the analysis of urban house prices.

Key words: mixed quantile regression, asymmetric Laplace distribution, expectation maximization (EM) algorithm

CLC Number:

O212.8

ZHANG Fagan, HE Youhua. Estimation of mixed quantile regression parameters based on an asymmetric Laplace distribution[J]. Journal of Shanghai University（Natural Science Edition）, 2021, 27(3): 601-610.

Figures/Tables 7

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References 12

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Estimation of mixed quantile regression parameters based on an asymmetric Laplace distribution

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