Comparison of Bayesian statistics and classical statistics in quantile regression analysis
10.7644/j.issn.1674-9960.2018.02.014
- VernacularTitle:贝叶斯统计和经典统计在分位数回归分析中的比较
- Author:
Heng-Ming GU
1
;
Liang-Ping HU
Author Information
1. 军事科学院研究生院生物医学统计学咨询中心
- Keywords:
Bayesian statistics;
classical statistics;
quantile regression;
fitting effect;
prediction effect;
cross-validation
- From:
Military Medical Sciences
2018;42(2):149-153
- CountryChina
- Language:Chinese
-
Abstract:
Objective To compare the Bayesian statistics and the classical statistics in the quantile regression analysis in order to select a more effective method .Methods The large sample data was chosen , and the QUANTREG procedure in SAS was used for the classical statistics and the MCMC procedure in SAS for the Bayesian one , respectively .Using ten-fold cross-validation method , the goodness of fitting of the models was appraised in terms of the fitted effect based on the training dataset and the predicted effect based on the predictive dataset .Results In most cases, the indexes of the quantile regression models in the classical statistics were slightly worse than those of the Bayesian one .In the ten-fold cross-validation of the partial samples as a training dataset , the fitting effect of the lower quartile ( Q1 ) and upper quartile ( Q3 ) of the Bayesian statistics was better than that of the classical one .However , the median ( Q2 ) fitting effect of the Bayesian statistics was slightly worse than that of the classical one .As for the prediction effect , the Bayesian statistical quantile regression model was superior to the classic one .Conclusion To expect high accuracy , such as the predictive effects and fitting effects of each quantile , the Bayesian quantile regression analysis should be chosen .If the major concern is the fitting effect of the median , careful selection from the approaches mentioned above is needed .If time and energy are limited, and the sample size is large enough , the classic statistical quantile regression analysis is a good choice .
