Foundations for Envelope Models and Methods

• 成果类型: Article
• 署名作者: Cook, R. Dennis; Zhang, Xin
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; Florida State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.983235
• 发表日期: 2015
• 页码: 599-611
• 关键词: regression

摘要

Envelopes were recently proposed by Cook, Li and Chiaromonte as a method for reducing estimative and predictive variations in multivariate linear regression. We extend their formulation, proposing a general definition of an envelope and a general framework for adapting envelope methods to any estimation procedure. We apply the new envelope methods to weighted least squares, generalized linear models and Cox regression. Simulations and illustrative data analysis show the potential for envelope methods to significantly improve standard methods in linear discriminant analysis, logistic regression and Poisson regression. Supplementary materials for this article are available online.