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題名 Improving unbiasedness of the proportional hazards model estimator with Cox and Snell’s bias approximation and jackknife resampling
作者 黃佳慧
Huang, Chia-Hui;Weng, Ruby Chiu-Hsing
貢獻者 統計系
關鍵詞 Bias calculation; counting process; Cox and Snell's formula; Jackknife; nonparametric maximum likelihood estimator; proportional hazards model
日期 2026-04
上傳時間 1-Apr-2026 16:37:09 (UTC+8)
摘要 Bias approximation has played an important role in the maximum likelihood estimation method, and numerous bias calculation techniques have been proposed under different contexts. For the semiparametric proportional hazards model, which is the standard regression method to study the time-to-event data, the existing work applied the bias formula and derived the approximate bias of Cox's estimator based on the partial likelihood function. In this work, we instead use the joint likelihood function and utilize the counting process approach to develop an approximate bias of Cox's estimator. Explicit expressions for the higher-order partial derivatives are derived, which facilitate the bias calculation techniques. We also incorporate the jackknife resampling method and propose a Jackknife-Cox-Snell method that processes the bias of Cox's estimator through two steps. The first step aims to remove the analytical terms derived from Cox and Snell's formula and the second step reduces the residual bias term. A comprehensive simulation study is performed to show the usefulness of the proposed bias-corrected method. We also apply the proposed method to two sets of survival data for comparison and illustration.
關聯 Journal of Statistical Computation and Simulation, Vol.96, No.6, pp.1303-1327
資料類型 article
DOI https://doi.org/10.1080/00949655.2025.2585359
dc.contributor 統計系
dc.creator (作者) 黃佳慧
dc.creator (作者) Huang, Chia-Hui;Weng, Ruby Chiu-Hsing
dc.date (日期) 2026-04
dc.date.accessioned 1-Apr-2026 16:37:09 (UTC+8)-
dc.date.available 1-Apr-2026 16:37:09 (UTC+8)-
dc.date.issued (上傳時間) 1-Apr-2026 16:37:09 (UTC+8)-
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=181855-
dc.description.abstract (摘要) Bias approximation has played an important role in the maximum likelihood estimation method, and numerous bias calculation techniques have been proposed under different contexts. For the semiparametric proportional hazards model, which is the standard regression method to study the time-to-event data, the existing work applied the bias formula and derived the approximate bias of Cox's estimator based on the partial likelihood function. In this work, we instead use the joint likelihood function and utilize the counting process approach to develop an approximate bias of Cox's estimator. Explicit expressions for the higher-order partial derivatives are derived, which facilitate the bias calculation techniques. We also incorporate the jackknife resampling method and propose a Jackknife-Cox-Snell method that processes the bias of Cox's estimator through two steps. The first step aims to remove the analytical terms derived from Cox and Snell's formula and the second step reduces the residual bias term. A comprehensive simulation study is performed to show the usefulness of the proposed bias-corrected method. We also apply the proposed method to two sets of survival data for comparison and illustration.
dc.format.extent 109 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Journal of Statistical Computation and Simulation, Vol.96, No.6, pp.1303-1327
dc.subject (關鍵詞) Bias calculation; counting process; Cox and Snell's formula; Jackknife; nonparametric maximum likelihood estimator; proportional hazards model
dc.title (題名) Improving unbiasedness of the proportional hazards model estimator with Cox and Snell’s bias approximation and jackknife resampling
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1080/00949655.2025.2585359
dc.doi.uri (DOI) https://doi.org/10.1080/00949655.2025.2585359