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中文摘要：
在多元线性回归模型的实际应用中,因受分析人员主观判断的影响,初始选取的自变量集合往往会包含过多的变量。本文利用施密特过程提出一种新的变量筛选方法,可以选择对因变量有显著解释作用的自变量,并且将没有解释作用的信息及冗余信息有效地分解出来并排除掉。将该方法应用于森林覆盖率影响因素分析,有效地进行了变量筛选,并得到了解释性很强同时拟合优度较高的模型结果。
英文摘要：
In application of multiple linear regression model,overfull variables will be selected into initial dependent variable set,due to subjective judgment of analysts.This paper proposes a new method of variable selection based on Schmidt process.The new method adopts independent variables which have significant explanation to dependent variables,decomposes and eliminates redundant information.This method is applied in the analysis of affecting factors of forest coverage.An effective variable selection is implem...
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