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Prior work has largely relied on linear models, which may inadequately capture relationships among multivariate covariates and thus hinder accurate change-point detection. To address this, we use a deep neural network to model covariate effects within the Cox framework and propose a maximum likelihood estimation procedure for the model. We establish asymptotic properties of the resulting estimators, including consistency, asymptotic independence, and semiparametric efficiency. Simulation studies indicate that the proposed inference procedure performs well in finite samples. An analysis of a breast cancer dataset is provided to illustrate the methodology.<\/jats:p>","DOI":"10.1007\/s10985-026-09689-y","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T05:33:27Z","timestamp":1770615207000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning for the change-point Cox model with current status data"],"prefix":"10.1007","volume":"32","author":[{"given":"Qiyue","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anyin","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingwei","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"9689_CR1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511624216","volume-title":"Neural network learning: theoretical foundations","author":"M Anthony","year":"1999","unstructured":"Anthony M, Bartlett PL (1999) Neural network learning: theoretical foundations. 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