收稿日期: 2019-03-13
网络出版日期: 2021-06-27
基金资助
国家自然科学基金资助项目(11471208)
Estimation of mixed quantile regression parameters based on an asymmetric Laplace distribution
Received date: 2019-03-13
Online published: 2021-06-27
利用非对称拉普拉斯分布提出一种新的混合分位数回归模型. 传统模型仅考虑位置参数, 而所提出模型同时考虑了位置参数和尺度参数, 并利用期望最大化(expectation maximization, EM)算法对模型参数进行估计. 数值分析结果表明, 参数估计的精度在各个 分位 数上均较为理想, 并且估计精度随着样本量的增加而提高. 最后运用所提出模 型及其算法对城市房价数据进行分析.
张发赶, 何幼桦 . 基于非对称拉普拉斯分布的混合分位数回归参数估计[J]. 上海大学学报(自然科学版), 2021 , 27(3) : 601 -610 . DOI: 10.12066/j.issn.1007-2861.2125
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.
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