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This article proposes a two-folded shrinkage procedure for simultaneous structure identification and variable selection of the semiparametric accelerated failure additive model with right-censored data, in which the nonparametric functions are addressed by spline approximation. Under some regularity conditions, the consistency of model structure identification is theoretically established in the sense that the proposed method can automatically separate the linear and zero components from the nonlinear ones with probability approaching to one. Detailed issues in computation and turning parameter selection are also discussed. Finally, we illustrate the proposed method by some simulation studies and two real data applications to the primary biliary cirrhosis data and skin cutaneous melanoma data. <\/jats:p>","DOI":"10.1177\/09622802231181224","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T15:44:38Z","timestamp":1687275878000},"page":"1527-1542","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Model detection for semiparametric accelerated failure additive model with right-censored data"],"prefix":"10.1177","volume":"32","author":[{"given":"Fang","family":"Lu","sequence":"first","affiliation":[{"name":"MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Huang","sequence":"additional","affiliation":[{"name":"MOE-LCSM, School of Mathematics and Statistics, Hunan Normal 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