摘要
随着计算机技术的不断发展,越来越多的高维数据产生,且在许多应用中,所调查的数据集显示的是异方差的状态。另一方面,模型中存在异常值可能会导致最小二乘估计量产生较大误差,特别是当误差不是高斯分布且分布尾部足够大时,不清楚变点前后两个时刻误差发生的变化,这时更适合考虑分位数回归。因此尝试利用贝叶斯方法建立贝叶斯单变点分层分位回归模型。利用shrinkage和diffusion先验,我们对变点进行了充分的后验推断,通过高效的Gibbs取样,同时得到了每段变量选择的后验概率。使用该方法,在计算上更加便捷有效。
With the continuous development of computer technology,a large amount of high-dimensional data is generated.And in many applications,the data set has heteroscedastic characteristics.On the other hand,if the assumptions on the first two moments of the model error are not satisfied,then the LS framework breaks down.The quantile regression is robust and allows relaxation of the two first moment conditions of the model error,especially when the error is not a Gaussian distribution and the tail of the distribution is large enough.So we try to use Bayesian method to establish Bayesian single-change point hierarchical quantile regression model.Using shrinkage and diffusion priors,we have performed sufficient posterior inference on the change points,and obtained the posterior probability of each segment variable selection at the same time through efficient Gibbs sampling.This method is more convenient and effective in calculation.
作者
慕娟
MU Juan(School of Statistics,Lanzhou University of Finance and Economics,Lanzhou 730020,China)
出处
《价值工程》
2020年第10期268-270,共3页
Value Engineering
关键词
高维数据
分位回归
贝叶斯方法
high-dimensional data
quantile regression
Bayesian method
