Subset Regression with Stepwise Directed Search
提出逐步定向搜索方法，结合前向选择和改进的后向消除，使用Mallows' Cp准则自动选择最优子集，计算量仅比无子集选择的估计多约三倍，实际数据表现优异。
SUMMARY Stepwise Directed Search is a combination of forward selection and a modification of backward elimination. Mallows' statistic C, is used as a criterion function in comparing subsets of different sizes. The method can select automatically the subset with the smallest C,. Alternatively a number of alternative good candidate subsets of any size can be obtained by using the results of the sweep operator efficiently. This limits the computational effort of subset selection to about three times more than required for estimation without subset selection. Subsets can be selected from any number of regressors, as long as the full set of regression variables can be swept without numerical difficulties. The performance of Stepwise Directed Search on practical data was excellent: it always found the minimum Cp subset and in every example but one it found the best subsets of all sizes.