The Impact of Sample-size and Sample-process on Several Usual Importance Evaluate Methods
- VernacularTitle:样本量及抽样过程对线性模型中自变量重要性估计方法的影响研究
- Author:
Lizhi WU
;
Xiaoxia JIA
;
Qijun SHEN
- Keywords:
Relative importance;
Sample-size;
Sample-process;
Monte Carlo simulation
- From:
Chinese Journal of Health Statistics
2017;34(2):210-213
- CountryChina
- Language:Chinese
-
Abstract:
Objective Implement random sample from a simulation population,to evaluate the The impact of samplesize and sample-process on several usual importance evaluate methods,observe the stability of those methods.Methods This study introduced existed importance methods,using PROC SURVEYSELECT procedure to sample a fixed population for 1000 times,generating 1000 same size sample,to evaluate the stability of relative importance methods.We sampled the population to generate datasets with different sample size to observe impact of sample-size on those methods.Results The sum of squared correlation coefficients' estimator is bigger than model R-square,squared standardized regression coefficients' sum is smaller.In contrary,sum of the Product Measure,Relative Weight and Dominance Analysis are extremely close to model R-square.When the sample size small than 1000,the estimator have obviously variation,but the variation decreased when the sample size rise up.Conclusion The dominance analysis has best stability,also has the best match of model R2 in those methods.
