Semiparametric fiducial inference for Cox models

• 成果类型: Article; Early Access
• 署名作者: Cui, Yifan; Hannig, Jan; Edlefsen, Paul
• 署名单位: Zhejiang University; University of North Carolina; University of North Carolina Chapel Hill; Fred Hutchinson Cancer Center
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412; 1467-9868
• DOI: 10.1093/jrsssb/qkag111
• 发表日期: 2026-07-01
• 关键词: Bernstein-von Mises theorem; conic optimization; cox model; fiducial inference; GIBBS SAMPLER; Semiparametric Model; constrained estimation; transformation models; variable selection; REGRESSION-MODEL; probability; parameter

摘要

R.A. Fisher introduced the fiducial distribution as a potential replacement for the Bayesian posterior distribution in the 1930s. During the past century, fiducial approaches have been explored in various parametric and nonparametric settings. However, to the best of our knowledge, no fiducial inference has been developed in the realm of semiparametric statistics. In this paper, we propose a novel fiducial approach for semiparametric models. In memory of Sir David Cox, who passed away in 2022, we use the Cox proportional hazards model, which is the most popular model for the analysis of survival data, as a running example. Other models and extensions are also discussed. In our experiments, we find that our method performs particularly well in situations where the maximum likelihood estimator fails.