SUFFICIENT DIMENSION REDUCTION BASED ON AN ENSEMBLE OF MINIMUM AVERAGE VARIANCE ESTIMATORS

• 成果类型: Article
• 署名作者: Yin, Xiangrong; Li, Bing
• 署名单位: University System of Georgia; University of Georgia; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/11-AOS950
• 发表日期: 2011
• 页码: 3392-3416
• 关键词: principal hessian directions; Sliced Inverse Regression; central subspace

摘要

We introduce a class of dimension reduction estimators based on an ensemble of the minimum average variance estimates of functions that characterize the central subspace, such as the characteristic functions, the Box-Cox transformations and wavelet basis. The ensemble estimators exhaustively estimate the central subspace without imposing restrictive conditions on the predictors, and have the same convergence rate as the minimum average variance estimates. They are flexible and easy to implement, and allow repeated use of the available sample, which enhances accuracy. They are applicable to both univariate and multivariate responses in a unified form. We establish the consistency and convergence rate of these estimators, and the consistency of a cross validation criterion for order determination. We compare the ensemble estimators with other estimators in a wide variety of models, and establish their competent performance.