ASYMPTOTIC THEORY FOR THE FIRST PROJECTIVE DIRECTION

• 成果类型: Article
• 署名作者: Akritas, Michael G.
• 署名单位: 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: 10.1214/16-AOS1438
• 发表日期: 2016
• 页码: 2161-2189
• 关键词: single-index models; Dimension Reduction; pursuit regression; estimators; coefficients

摘要

For a response variable Y, and a d dimensional vector of covariates X, the first projective direction, V, is defined as the direction that accounts for the most variability in Y. The asymptotic distribution of an estimator of a trimmed version of V has been characterized only under the assumption of the single index model (SIM). This paper proposes the use of a flexible trimming function in the objective function, which results in the consistent estimation of V. It also derives the asymptotic normality of the proposed estimator, and characterizes the components of the asymptotic variance which vanish when the SIM holds.