Mixed-rates asymptotics

• 成果类型: Article
• 署名作者: Radchenko, Peter
• 署名单位: University of Southern California
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053607000000668
• 发表日期: 2008
• 页码: 287-309
• 关键词: theorem; Lasso

摘要

A general method is presented for deriving the limiting behavior of estimators that are defined as the values of parameters optimizing an empirical criterion function. The asymptotic behavior of such estimators is typically deduced from uniform limit theorems for rescaled and reparametrized criterion functions. The new method can handle cases where the standard approach does not yield the complete limiting behavior of the estimator. The asymptotic analysis depends on a decomposition of criterion functions into sums of components with different rescalings. The method is explained by examples from Lasso-type estimation, k-means clustering, Shorth estimation and partial linear models.