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 .
ABSTRACT
Objective To compare the difference of effect between principal component regression analysis and projection pursuit regression analysis when collinearity exists in data.Methods Evaluating the advantages and disadvantages of the two modeling methods by using the actual data on two aspects:the fitting effect and the predicting effect.Results The principal component regression model showed that the coefficient of determination was 0.8172,the mean of absolute relative error was 6.42％ and the mean square of prediction error was 0.61.The projection pursuit regression model,on the other hand,showed that the coefficient of determination ranged from 0.8851 to 0.9944,the mean of absolute relative error ranged from1.11％ to 4.81％ and the mean square of prediction error ranged from 0.03 to 0.38.Conclusion The analysis results based on the actual data with collinearity indicate that the projection pursuit regression analysis outperforms the principal component regression analysis both in fitting and predicting effect.