{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T06:13:43Z","timestamp":1777875223340,"version":"3.51.4"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72531002"],"award-info":[{"award-number":["72531002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.neucom.2026.133086","type":"journal-article","created":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T15:55:32Z","timestamp":1771084532000},"page":"133086","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Neural network for censored expectile regression based on data augmentation"],"prefix":"10.1016","volume":"676","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7605-7116","authenticated-orcid":false,"given":"Wei","family":"Cao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7205-3844","authenticated-orcid":false,"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0005","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/BF00773468","article-title":"Relating quantiles and expectiles under weighted-symmetry","volume":"47","author":"Abdous","year":"1995","journal-title":"Ann. Inst. Stat. Math."},{"issue":"30","key":"10.1016\/j.neucom.2026.133086_bib0010","article-title":"Coxse: exploring the potential of self-explaining neural networks with Cox proportional hazards model for survival analysis","volume":"333","author":"Alabdallah","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"551","key":"10.1016\/j.neucom.2026.133086_bib0015","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1080\/01621459.2025.2516820","article-title":"Communication-efficient distributed estimation and inference for cox\u2019s model","volume":"120","author":"Bayle","year":"2025","journal-title":"J. Am. Stat. Assoc."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/econometrics13010001","article-title":"Relationship between coefficients in parametric survival models for exponentially distributed survival time\u2014registered unemployment in Poland","volume":"13","author":"Bieszk-Stolorz","year":"2025","journal-title":"Econometrics"},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0025","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.13087","article-title":"Expectile regression forest: a new nonparametric expectile regression model","volume":"40","author":"Cai","year":"2023","journal-title":"Expert Systems"},{"key":"10.1016\/j.neucom.2026.133086_bib0030","doi-asserted-by":"crossref","DOI":"10.1002\/bimj.70118","article-title":"Expectile regression for censored data based on data augmentation","author":"Cao","year":"2026","journal-title":"Biom. J."},{"key":"10.1016\/j.neucom.2026.133086_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2020.101054","article-title":"Predictive maintenance using Cox proportional hazard deep learning","volume":"44","author":"Chen","year":"2020","journal-title":"Adv. Eng. Inform."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0040","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/10618600.2024.2365740","article-title":"Interval-censored linear quantile regression","volume":"34","author":"Choi","year":"2024","journal-title":"J. Comput. Graph. Stat."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0045","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10985-024-09643-w","article-title":"Right-censored models by the expectile method","volume":"31","author":"Ciuperca","year":"2025","journal-title":"Lifetime Data Anal."},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0050","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and Life-Tables","volume":"34","author":"Cox","year":"1972","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"issue":"3","key":"10.1016\/j.neucom.2026.133086_bib0055","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1093\/biostatistics\/kxac009","article-title":"A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors","volume":"24","author":"Crowther","year":"2023","journal-title":"Biostatistics"},{"issue":"4","key":"10.1016\/j.neucom.2026.133086_bib0060","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control Signals Syst."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0065","first-page":"165","article-title":"Optimal distributed online prediction using mini-batches","volume":"13","author":"Dekel","year":"2012","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0070","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/sim.4780140108","article-title":"A neural network model for survival data","volume":"14","author":"Faraggi","year":"1995","journal-title":"Stat. Med."},{"key":"10.1016\/j.neucom.2026.133086_bib0075","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.csda.2016.11.010","article-title":"An SVM-like approach for expectile regression","volume":"109","author":"Farooq","year":"2017","journal-title":"Comput. Stat. Data Anal."},{"issue":"15\u201317","key":"10.1016\/j.neucom.2026.133086_bib0080","article-title":"A latent-class model for time-to-event outcomes and high-dimensional imaging data","volume":"44","author":"Feng","year":"2025","journal-title":"Stat. Med."},{"key":"10.1016\/j.neucom.2026.133086_bib0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.118986","article-title":"Damcqrnn: an approach to censored monotone composite quantile regression neural network estimation","volume":"638","author":"Hao","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2026.133086_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119097","article-title":"Data augmentation based estimation for the censored quantile regression neural network model","volume":"214","author":"Hao","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133086_bib0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109381","article-title":"Data augmentation based estimation for the censored composite quantile regression neural network model","volume":"127","author":"Hao","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2026.133086_bib0100","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1016\/S1573-4412(05)80007-8","article-title":"Applied nonparametric methods","volume":"4","author":"H\u00e4rdle","year":"1994","journal-title":"Handbook of econometrics"},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0105","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/sim.4780030207","article-title":"Regression modelling strategies for improved prognostic prediction","volume":"3","author":"Harrell Jr","year":"1984","journal-title":"Stat. Med."},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0110","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","article-title":"Approximation capabilities of multilayer feedforward networks","volume":"4","author":"Hornik","year":"1991","journal-title":"Neural networks"},{"issue":"5","key":"10.1016\/j.neucom.2026.133086_bib0115","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural networks"},{"issue":"4","key":"10.1016\/j.neucom.2026.133086_bib0120","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1002\/sim.2581","article-title":"Multiple imputation for interval censored data with auxiliary variables","volume":"26","author":"Hsu","year":"2007","journal-title":"Stat. Med."},{"issue":"3","key":"10.1016\/j.neucom.2026.133086_bib0125","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.19139\/soic-2310-5070-2627","article-title":"A novel accelerated failure time model with risk analysis under actuarial data, censored and uncensored application","volume":"14","author":"Ibrahim","year":"2025","journal-title":"Stat. Optim. Inf. Comput."},{"key":"10.1016\/j.neucom.2026.133086_bib0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.csda.2021.107323","article-title":"Deep learning for quantile regression under right censoring: Deepquantreg","volume":"165","author":"Jia","year":"2022","journal-title":"Comput. Stat. Data Anal."},{"key":"10.1016\/j.neucom.2026.133086_bib0135","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.neucom.2017.03.040","article-title":"Expectile regression neural network model with applications","volume":"247","author":"Jiang","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133086_bib0140","series-title":"The Statistical Analysis of Failure Time Data","author":"Kalbfleisch","year":"2002"},{"issue":"282","key":"10.1016\/j.neucom.2026.133086_bib0145","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","article-title":"Nonparametric estimation from incomplete observations","volume":"53","author":"Kaplan","year":"1958","journal-title":"J. Am. Stat. Assoc."},{"key":"10.1016\/j.neucom.2026.133086_bib0150","series-title":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)","first-page":"2880","article-title":"Mini-batch gradient descent: faster convergence under data sparsity","author":"Khirirat","year":"2017"},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0155","first-page":"646","article-title":"Economic duration data and hazard functions","volume":"26","author":"Kiefer","year":"1988","journal-title":"J. Econ. Lit."},{"issue":"28","key":"10.1016\/j.neucom.2026.133086_bib0160","doi-asserted-by":"crossref","first-page":"5331","DOI":"10.1002\/sim.10235","article-title":"Deep neural network-based accelerated failure time models using rank loss","volume":"43","author":"Kim","year":"2024","journal-title":"Stat. Med."},{"issue":"4","key":"10.1016\/j.neucom.2026.133086_bib0165","doi-asserted-by":"crossref","first-page":"2831","DOI":"10.1214\/25-AOAS2103","article-title":"Estimating the time-to-event distribution for loan-level data within a consumer auto loan asset-backed security","volume":"19","author":"Lautier","year":"2025","journal-title":"Ann. Appl. Stat."},{"issue":"17","key":"10.1016\/j.neucom.2026.133086_bib0170","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.1002\/sim.4503","article-title":"Multiple imputation for left-censored biomarker data based on Gibbs sampling method","volume":"31","author":"Lee","year":"2012","journal-title":"Stat. Med."},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0175","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1007\/s10614-023-10473-5","article-title":"Data augmentation based quantile regression estimation for censored partially linear additive model","volume":"64","author":"Li","year":"2024","journal-title":"Comput. Econ."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0180","doi-asserted-by":"crossref","DOI":"10.1093\/biomet\/asae065","article-title":"Doubly robust estimation under a possibly misspecified marginal structural cox model","volume":"112","author":"Luo","year":"2025","journal-title":"Biometrika"},{"issue":"4","key":"10.1016\/j.neucom.2026.133086_bib0185","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s00181-006-0065-6","article-title":"Censored quantile regressions and the length of unemployment periods in West Germany","volume":"31","author":"L\u00fcdemann","year":"2006","journal-title":"Empir. Econ."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0190","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.jeconom.2020.12.005","article-title":"Censored quantile regression survival models with a cure proportion","volume":"226","author":"Narisetty","year":"2022","journal-title":"J. Econom."},{"issue":"4","key":"10.1016\/j.neucom.2026.133086_bib0195","doi-asserted-by":"crossref","first-page":"819","DOI":"10.2307\/1911031","article-title":"Asymmetric least squares estimation and testing","volume":"55","author":"Newey","year":"1987","journal-title":"Econometrica"},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0200","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1080\/07350015.1985.10509439","article-title":"How long is a spell of unemployment? Illusions and biases in the use of CPS data","volume":"3","author":"Nicholas M. Kiefer","year":"1985","journal-title":"J. Bus. Econ. Stat."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0205","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s11634-020-00390-y","article-title":"A novel semi-supervised support vector machine with asymmetric squared loss","volume":"15","author":"Pei","year":"2021","journal-title":"Adv. Data Anal. Classif."},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.jeconom.2022.08.009","article-title":"A latent class cox model for heterogeneous time-to-event data","volume":"239","author":"Pei","year":"2024","journal-title":"Journal of Econometrics"},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0215","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-39170-x","article-title":"Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data","volume":"13","author":"Rahman","year":"2023","journal-title":"Sci. Rep."},{"issue":"25","key":"10.1016\/j.neucom.2026.133086_bib0220","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.1002\/sim.9137","article-title":"Weighted expectile regression for right-censored data","volume":"40","author":"Seipp","year":"2021","journal-title":"Stat. Med."},{"issue":"4","key":"10.1016\/j.neucom.2026.133086_bib0225","first-page":"59","article-title":"Survival analysis in credit risk management: a review study","volume":"20","author":"Shang","year":"2025","journal-title":"J. Credit Risk"},{"key":"10.1016\/j.neucom.2026.133086_bib0230","series-title":"Conference on Learning Theory","first-page":"1517","article-title":"Benefits of depth in neural networks","author":"Telgarsky","year":"2016"},{"key":"10.1016\/j.neucom.2026.133086_bib0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107364","article-title":"Deep Huber quantile regression networks","volume":"187","author":"Tyralis","year":"2025","journal-title":"Neural Netw."},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0240","doi-asserted-by":"crossref","DOI":"10.1093\/biomtc\/ujae028","article-title":"Causal inference for time-to-event data with a cured subpopulation","volume":"80","author":"Wang","year":"2024","journal-title":"Biometrics"},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0245","first-page":"527","article-title":"Tobit quantile regression of left-censored longitudinal data with informative observation times","volume":"28","author":"Wang","year":"2018","journal-title":"Stat. Sin."},{"issue":"14\u201315","key":"10.1016\/j.neucom.2026.133086_bib0250","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1002\/sim.4780111409","article-title":"The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis","volume":"11","author":"Wei","year":"1992","journal-title":"Stat. Med."},{"issue":"5","key":"10.1016\/j.neucom.2026.133086_bib0255","doi-asserted-by":"crossref","DOI":"10.1002\/bimj.202200127","article-title":"A censored quantile regression approach for relative survival analysis: relative survival quantile regression","volume":"65","author":"Williamson","year":"2023","journal-title":"Biom. J."},{"issue":"504","key":"10.1016\/j.neucom.2026.133086_bib0260","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1080\/01621459.2013.837368","article-title":"Cure rate quantile regression for censored data with a survival fraction","volume":"108","author":"Wu","year":"2013","journal-title":"J. Am. Stat. Assoc."},{"issue":"1","key":"10.1016\/j.neucom.2026.133086_bib0265","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1111\/biom.12574","article-title":"Multiple imputation for cure rate quantile regression with censored data: multiple imputation for cure rate quantile regression","volume":"73","author":"Wu","year":"2017","journal-title":"Biometrics"},{"key":"10.1016\/j.neucom.2026.133086_bib0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109672","article-title":"Improving failure modeling for gas transmission pipelines: a survival analysis and machine learning integrated approach","volume":"241","author":"Xiao","year":"2024","journal-title":"Rel. Eng. Syst. Saf."},{"key":"10.1016\/j.neucom.2026.133086_bib0275","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1007\/s12204-023-2669-9","article-title":"Lifespan prediction of electronic card in nuclear power plant based on few samples","volume":"30","author":"Xu","year":"2025","journal-title":"J. Shanghai Jiaotong Univ. (Sci.)"},{"issue":"6","key":"10.1016\/j.neucom.2026.133086_bib0280","doi-asserted-by":"crossref","DOI":"10.1002\/sam.70002","article-title":"Nonparametric expectile regression meets deep neural networks: a robust nonlinear variable selection method","volume":"17","author":"Yang","year":"2024","journal-title":"Stat. Anal. Data Min.: ASA Data Sci. J."},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0285","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/10618600.2017.1385469","article-title":"A new approach to censored quantile regression estimation","volume":"27","author":"Yang","year":"2018","journal-title":"J. Comput. Graph. Stat."},{"issue":"7","key":"10.1016\/j.neucom.2026.133086_bib0290","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1080\/00949655.2013.876024","article-title":"Nonparametric multiple expectile regression via ER-boost","volume":"85","author":"Yang","year":"2015","journal-title":"J. Stat. Comput. Simul."},{"key":"10.1016\/j.neucom.2026.133086_bib0295","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.neunet.2017.07.002","article-title":"Error bounds for approximations with deep ReLU networks","volume":"94","author":"Yarotsky","year":"2017","journal-title":"Neural networks"},{"key":"10.1016\/j.neucom.2026.133086_bib0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.csda.2024.107978","article-title":"Rank-based sequential feature selection for high-dimensional accelerated failure time models with main and interaction effects","volume":"197","author":"Yu","year":"2024","journal-title":"Comput. Stat. Data Anal."},{"issue":"2","key":"10.1016\/j.neucom.2026.133086_bib0305","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1111\/biom.13309","article-title":"Quantile regression for survival data with covariates subject to detection limits","volume":"77","author":"Yu","year":"2021","journal-title":"Biometrics"},{"key":"10.1016\/j.neucom.2026.133086_bib0310","doi-asserted-by":"crossref","first-page":"5100","DOI":"10.1002\/sim.10221","article-title":"Weighted expectile regression neural networks for right censored data","volume":"43","author":"Zhang","year":"2024","journal-title":"Stat. Med."},{"key":"10.1016\/j.neucom.2026.133086_bib0315","first-page":"15111","article-title":"Deep extended hazard models for survival analysis","volume":"34","author":"Zhong","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226004832?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226004832?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T21:21:44Z","timestamp":1777584104000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226004832"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":63,"alternative-id":["S0925231226004832"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133086","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Neural network for censored expectile regression based on data augmentation","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133086","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133086"}}