Consistent Variable Selection in Linear Models
线性模型中的一致变量选择
A method of estimating linear model dimension and variable selection is proposed. This new criterion, which generalizes the Cp criterion, the Akaike information criterion (AIC), the Bayes information criterion, and the phiv criterion and is consistent under certain conditions, is based on a new class of penalty functions and a procedure of sorting covariates based on t-statistics. In the course of introducing this method, we discuss the important role of the penalty function…