{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:53:30Z","timestamp":1782392010207,"version":"3.54.5"},"reference-count":28,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12031016"],"award-info":[{"award-number":["12031016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12171328"],"award-info":[{"award-number":["12171328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["Z210003"],"award-info":[{"award-number":["Z210003"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Stat Methods Med Res"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:p>We consider simultaneous variable selection and estimation for a deep neural network-based partially linear Cox model and propose a novel penalized approach. In particular, a two-step iterative algorithm is developed with the use of the minimum information criterion to ensure sparse estimation. The proposed method circumvents the curse of dimensionality while facilitating the interpretability of linear covariate effects on survival, and the algorithm greatly reduces the computational burden by avoiding the need to select the optimal tuning parameters that is usually required by many other popular penalties. The convergence rate and asymptotic properties of the resulting estimator are established along with the consistency of variable selection. The performance of the procedure is demonstrated through extensive simulation studies and an application to a myeloma dataset.<\/jats:p>","DOI":"10.1177\/09622802251322988","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T20:42:48Z","timestamp":1742503368000},"page":"783-795","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Simultaneous variable selection and estimation for a partially linear Cox model"],"prefix":"10.1177","volume":"34","author":[{"given":"Tingting","family":"Cai","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Capital Normal University, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqi","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Capital Normal University, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4463-004X","authenticated-orcid":false,"given":"Tao","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Capital Normal University, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6946-671X","authenticated-orcid":false,"given":"Jianguo","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Missouri, Columbia, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1017939141"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9938"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1214\/21-AOS2153"},{"key":"e_1_3_5_5_2","first-page":"1875","article-title":"Nonparametric regression using deep neural networks with ReLU activation function","volume":"48","author":"Schmidt-Hieber J","year":"2020","unstructured":"Schmidt-Hieber J. Nonparametric regression using deep neural networks with ReLU activation function. Ann Stat 2020; 48: 1875\u20131897.","journal-title":"Ann Stat"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1747"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1032181158"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382273"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS683"},{"key":"e_1_3_5_11_2","first-page":"929","article-title":"Variable selection and estimation with the seamless- L0 penalty","volume":"23","author":"Dicker L","year":"2013","unstructured":"Dicker L, Huang B, Lin X. Variable selection and estimation with the seamless- L0 penalty. Stat Sin 2013; 23: 929\u2013962.","journal-title":"Stat Sin"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/4310634"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1537922"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomtc\/ujad024"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12484"},{"key":"e_1_3_5_16_2","unstructured":"Nair V Hinton GE. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10). pp.807\u2013814."},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.1201\/b18401"},{"key":"e_1_3_5_18_2","unstructured":"Kingma DP Ba J. Adam: A method for stochastic optimization 2014. arXiv:1412.6980."},{"key":"e_1_3_5_19_2","volume-title":"Numerical Optimization","author":"Nocedal J","year":"2016","unstructured":"Nocedal J, Wright SJ. Numerical Optimization. New York: Springer, 2016."},{"key":"e_1_3_5_20_2","volume-title":"A Software Package for Sequential Quadratic Programming","author":"Kraft D","year":"1988","unstructured":"Kraft D. A Software Package for Sequential Quadratic Programming. K\u00f6ln: Wiss. Berichtswesen d. DFVLR, 1988."},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1974.1100705"},{"key":"e_1_3_5_22_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1982.03320430047030"},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2427"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2009-12-261032"},{"key":"e_1_3_5_25_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19990915\/30)18:17\/18<2529::AID-SIM274>3.0.CO;2-5"},{"key":"e_1_3_5_26_2","volume-title":"The Statistical Analysis of Interval-censored Failure Time Data","author":"Sun J","year":"2006","unstructured":"Sun J. The Statistical Analysis of Interval-censored Failure Time Data. New York: Springer, 2006."},{"key":"e_1_3_5_27_2","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13734"},{"key":"e_1_3_5_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01680-1"},{"key":"e_1_3_5_29_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.10031"}],"container-title":["Statistical Methods in Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09622802251322988","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09622802251322988","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09622802251322988","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T07:32:40Z","timestamp":1748417560000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09622802251322988"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,20]]},"references-count":28,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["10.1177\/09622802251322988"],"URL":"https:\/\/doi.org\/10.1177\/09622802251322988","relation":{},"ISSN":["0962-2802","1477-0334"],"issn-type":[{"value":"0962-2802","type":"print"},{"value":"1477-0334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,20]]}}}