{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:43:11Z","timestamp":1776919391335,"version":"3.51.2"},"reference-count":12,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the advancement of information technology, large-scale data have become increasingly common. Subsampling methods for the statistical analysis of such data require computing the sampling probability for each observation, a process that can be computationally intensive. In this paper, we extend the perturbed subsampling approach to the Cox proportional hazards model, a widely used method in survival analysis to address the statistical analysis of large-scale survival data. Specifically, we propose a perturbed subsampling algorithm for this model. The effectiveness of the proposed method is evaluated through simulation studies and real-data analysis.<\/jats:p>","DOI":"10.3390\/e28040476","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T14:53:23Z","timestamp":1776696803000},"page":"476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Perturbation Subsampling Method for Massive Censored Data"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8853-0481","authenticated-orcid":false,"given":"Yan","family":"Tian","sequence":"first","affiliation":[{"name":"School of Mathematics, Liaoning Normal University, Dalian 116081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7527-1971","authenticated-orcid":false,"given":"Jiaxin","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mathematics, Liaoning Normal University, Dalian 116081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1002\/wics.1324","article-title":"Leveraging for big data regression","volume":"7","author":"Ma","year":"2015","journal-title":"Wiley Interdiscip. 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