LOCALIZED MODEL SELECTION FOR REGRESSION
LOCALIZED MODEL SELECTION FOR REGRESSION
复制标题
回归的本地化模型选择
作者：
Yuhong Yang
模型/过程选择的研究主要集中在全局选择单个模型上。然而，在许多应用中，特别是对于高维或复杂数据，候选过程的相对性能通常取决于位置，并且当允许根据位置选择模型时，全局最优过程通常可以被改进。我们考虑了本地化的模型选择方法，并推导了它们的理论性质。我们感谢三位裁判和编辑对改进论文提出了有益的意见。
Research on model/procedure selection has focused on selecting a single model globally. In many applications, especially for high-dimensional or complex data, however, the relative performance of the candidate procedures typically depends on the location, and the globally best procedure can often be improved when selection of a model is allowed to depend on location. We consider localized model selection methods and derive their theoretical properties.This research was supported by U.S. National Science Foundation CAREER Grant DMS0094323. We thank three referees and the editors for helpful comments on improving the paper.