ADAPTING FOR THE MISSING LINK

• 成果类型: Article
• 署名作者: WEISBERG, S; WELSH, AH
• 署名单位: Australian National University; Australian National University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176325749
• 发表日期: 1994
• 页码: 1674-1700
• 关键词: logistic-regression; diagnostics

摘要

We consider the fitting of generalized linear models in which the link function is assumed to be unknown, and propose the following computational method: First, estimate regression coefficients using the canonical link. Then, estimate the link via a kernel smoother, treating the direction in the predictor space determined by the regression coefficients as known. Then reestimate the direction using the estimated link and alternate between these two steps. We show that under fairly general conditions, n(1/2)-consistent estimates of the direction are obtained. A small Monte Carlo study is presented.