Markov chain marginal bootstrap

• 成果类型: Article
• 署名作者: He, XM; Hu, FF
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214502388618591
• 发表日期: 2002
• 页码: 783-795
• 关键词: LINEAR-REGRESSION; M-ESTIMATORS

摘要

Markov chain marginal bootstrap (MCMB) is a new method for constructing confidence intervals or regions for maximum likelihood estimators of certain parametric models and for a wide class of M estimators of linear regression. The MCMB method distinguishes itself from the usual bootstrap methods in two important aspects: it involves solving only one-dimensional equations for parameters of any dimension and produces a Markov chain rather than a (conditionally) independent sequence. It is designed to alleviate computational burdens often associated with bootstrap in high-dimensional problems. The validity of MCMB is established through asymptotic analyses and illustrated with empirical and simulation studies for linear regression and generalized linear models.