Random sieve likelihood and general regression models

• 成果类型: Article
• 署名作者: Shen, XT; Shi, J; Wong, WH
• 署名单位: University System of Ohio; Ohio State University; Chinese Academy of Sciences; University of California System; University of California Los Angeles
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2669998
• 发表日期: 1999
• 页码: 835-846
• 关键词: empirical likelihood; censored data; CONVERGENCE; rates

摘要

Consider a semiparametric regression model Y = f (theta, X, epsilon), where f is a known function, theta is an unknown vector, epsilon consists of a random error and possibly of some unobserved variables, and the distribution F(.) of (epsilon, X) is unspecified. This article introduces, in a general setting, new methodology for estimating theta and F(.). The proposed method constructs a profile likelihood defined on random-level sets (a random sieve). The proposed method is related to empirical likelihood but is more generally applicable. Four examples are discussed, including a quadratic model, high-dimensional semiparametric regression, a nonparametric random-effects model, and linear regression with right-censored data. Simulation results and asymptotic analysis support the utility and effectiveness of the proposed method.