摘要
本文采用非对称指数幂分布(AEP)与非对称拉普拉斯分布(ALD)对分位数回归模型的误差项进行假定,对此进行贝叶斯分位数回归的参数估计。针对参数后验密度的复杂性,采用吉布斯抽样算法,对ALD分布和AEP分布进行后验参数抽样。通过数值模拟结果可知,服从AEP分布的误差假定对数据的适应性比ALD分布的要强。
In this paper, the asymmetric exponential power distribution (AEP) and asymmetric Laplace distribution (ALD) are used to make assumptions about the error terms of the quantile regression model, and the parameters of Bayesian quantile regression are estimated for this. Aiming at the complexity of the posterior density of parameters, the Gibbs sampling algorithm is used to sample the posterior parameters of the ALD distribution and AEP distribution. According to the numerical simulation results, the error following the AEP distribution is assumed to be more adaptable to the data than the ALD distribution.
出处
《应用数学进展》
2020年第7期1054-1065,共12页
Advances in Applied Mathematics
关键词
分位数回归
非对称指数幂分布
非对称拉普拉斯分布
吉布斯抽样
Quantile Regression
Asymmetric Exponential Power Distribution
Asymmetric Laplace Distribution
Gibbs Sampling
