A Comparative Study on the Effect of Principal Component Regression Analysis and Projection Pursuit Regression Analysis Applied to the Data with Collinearity
- VernacularTitle:数据存在共线性时采用主成分回归分析与投影寻踪回归分析的效果比较
- Author:
Wan HU
;
Yansong SUN
;
Liangping HU
- Keywords:
Principal component regression analysis;
Projection pursuit regression analysis;
Collinearity;
The fitting effect;
The predicting effect
- From:
Chinese Journal of Health Statistics
2017;34(2):192-195
- CountryChina
- Language:Chinese
-
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.
