A Fast Model Selection Procedure for Large Families of Models
提出一种从大型模型族中高效选择模型的程序，基于接受包含某模型的全部模型、拒绝某模型的所有子模型的原则，可应用于多元回归中的变量选择等问题。
Abstract An efficient procedure for model selection from large families of models is described. It is closely related to the all possible models approach but is considerably faster. It is based on two principles: first, if a model is accepted, then all models that include it are considered to be accepted; second, if a model is rejected, then all of its submodels are considered to be rejected. Application of the procedure to variable selection in multiple regression is illustrated. General algorithms are described that enable the procedure to be applied to any family of models that forms a lattice. As an example, a problem in multiple comparisons is considered.