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中文摘要：
将Gram-Schmidt过程运用到logistical回归模型当中,提出Schmidt-logistical回归方法,该方法在自变量集合中选择对模型解释性最强的信息,删除对因变量无显著解释作用的信息以及重复解释的信息,并把挑选出来的解释变量集合变换成若干直交变量。这样,一方面实现了logistical回归中的变量筛选;同时,Gram-Schmidt正交化过程的信息分解结构清晰,使得回归模型的解释更加容易。将该方法应用于股票投资风格分析中,有效地进行了变量筛选,实现了股票成长/价值属性的快速判断。
英文摘要：
This paper applies the Gram-Schmidt process into the logistical regression model and proposes the Schmidt-logistical regression method,which chooses the most explainable information from the original independent variable set,omits useless or redundant information,and transforms the selected independent variables into orthogonal ones.In this way,this method can not only realize variable selection in logistic regression,but also obtains a clear-structured model,which can be easily explained.This method is applied in the analysis of style investing,effectively performing variables selection and judging growth/value property of stocks.
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