Rank estimation for the accelerated failure time model with partially interval-censored data

[2503.11268] Rank estimation for the accelerated failure time model with partially interval-censored data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2503.11268 (stat) [Submitted on 14 Mar 2025 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: Rank estimation for the accelerated failure time model with partially interval-censored data Authors: Taehwa Choi , Sangbum Choi , Dipankar Bandyopadhyay View a PDF of the paper titled Rank estimation for the accelerated failure time model with partially interval-censored data, by Taehwa Choi and 2 other authors View PDF HTML (experimental) Abstract: This paper presents a unified rank-based inferential procedure for fitting the accelerated failure time model to partially interval-censored data. A Gehan-type monotone estimating function is constructed based on the idea of the familiar weighted log-rank test, and an extension to a general class of rank-based estimating functions is suggested. The proposed estimators can be obtained via linear programming and are shown to be consistent and asymptotically normal via standard empirical process theory. Unlike common maximum likelihood-based estimators for partially interval-censored regression models, our approach can directly provide a regression coefficient estimator without involving a complex nonparametric estimation of the underlying residual distribution function. An efficient variance estimation procedure for the regression coefficient estimator is considered. Moreover, we extend the proposed rank-based procedure to the linear regression analysis of multivariate clustered partially interval-censored data. The finite-sample operating characteristics of our approach are examined via simulation studies. Data example from a colorectal cancer study illustrates the practical usefulness of the method. Comments: Accepted in Statistica Sinica Subjects: Methodology (stat.ME) ; Computation (stat.CO) Cite as: arXiv:2503.11268 [stat.ME] (or arXiv:2503.11268v2 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2503.11268 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.5705/ss.202024.0003 Focus to learn more DOI(s) linking to related resources Submission history From: Taehwa Choi [ view email ] [v1] Fri, 14 Mar 2025 10:24:51 UTC (528 KB) [v2] Sat, 11 Apr 2026 08:24:17 UTC (418 KB) Full-text links: Access Paper: View a PDF of the paper titled Rank estimation for the accelerated failure time model with partially interval-censored data, by Taehwa Choi and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2025-03 Change to browse by: stat stat.CO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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