A Newton-Type Method for ℓ0-Regularized Accelerated Failure Time Model Under the Case–Cohort Design

The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies. In this paper, we study the high-dimensional accelerated failure time (AFT) model under the case–cohort design. Based on ℓ 0 -regularization and a newly defined weight function, we propo...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Acta mathematica Sinica. English series Ročník 41; číslo 9; s. 2275 - 2300
Hlavní autori:	Liu, Yanyan, Tian, Ke, Wang, Danlu, Zhang, Jing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025 Springer Nature B.V
Predmet:	Algorithms Failure times Gene expression Least squares method Mathematics Mathematics and Statistics Newton methods Parameter estimation Regularization Weighting functions 62N01 62P10 62N02 newton-type method regularization case–cohort design 62D99 Accelerated failure time model support detection and root finding algorithm
ISSN:	1439-8516, 1439-7617
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies. In this paper, we study the high-dimensional accelerated failure time (AFT) model under the case–cohort design. Based on ℓ 0 -regularization and a newly defined weight function, we propose a weighted least squares procedure for variable selection and parameter estimation. Computationally, we develop a support detection and root finding (SDAR) algorithm, where the support is first determined based on the primal and dual information, then the estimator is obtained by solving the weighted least squares problem restricted to the estimated support. We show the proposed algorithm is essentially one Newton-type algorithm, thus it is more efficient and stable compared with other regularized methods. Theoretically, we establish a sharp error bound for the solution sequences generated from the proposed method. Furthermore, we propose an adaptive version of the proposed SDAR algorithm, which determines the support size of the estimated coefficient in a data-driven manner. Extensive simulation studies demonstrate the superior performance of the proposed procedures, especially for the computational efficiency. As an illustration, we apply the proposed method to a malignant breast tumor gene expression data.
AbstractList	The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies. In this paper, we study the high-dimensional accelerated failure time (AFT) model under the case–cohort design. Based on ℓ 0 -regularization and a newly defined weight function, we propose a weighted least squares procedure for variable selection and parameter estimation. Computationally, we develop a support detection and root finding (SDAR) algorithm, where the support is first determined based on the primal and dual information, then the estimator is obtained by solving the weighted least squares problem restricted to the estimated support. We show the proposed algorithm is essentially one Newton-type algorithm, thus it is more efficient and stable compared with other regularized methods. Theoretically, we establish a sharp error bound for the solution sequences generated from the proposed method. Furthermore, we propose an adaptive version of the proposed SDAR algorithm, which determines the support size of the estimated coefficient in a data-driven manner. Extensive simulation studies demonstrate the superior performance of the proposed procedures, especially for the computational efficiency. As an illustration, we apply the proposed method to a malignant breast tumor gene expression data. The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies. In this paper, we study the high-dimensional accelerated failure time (AFT) model under the case–cohort design. Based on ℓ0-regularization and a newly defined weight function, we propose a weighted least squares procedure for variable selection and parameter estimation. Computationally, we develop a support detection and root finding (SDAR) algorithm, where the support is first determined based on the primal and dual information, then the estimator is obtained by solving the weighted least squares problem restricted to the estimated support. We show the proposed algorithm is essentially one Newton-type algorithm, thus it is more efficient and stable compared with other regularized methods. Theoretically, we establish a sharp error bound for the solution sequences generated from the proposed method. Furthermore, we propose an adaptive version of the proposed SDAR algorithm, which determines the support size of the estimated coefficient in a data-driven manner. Extensive simulation studies demonstrate the superior performance of the proposed procedures, especially for the computational efficiency. As an illustration, we apply the proposed method to a malignant breast tumor gene expression data.
Author	Zhang, Jing Tian, Ke Liu, Yanyan Wang, Danlu
Author_xml	– sequence: 1 givenname: Yanyan surname: Liu fullname: Liu, Yanyan organization: School of Mathematics and Statistics, Wuhan University – sequence: 2 givenname: Ke surname: Tian fullname: Tian, Ke organization: School of Mathematics and Statistics, Wuhan University – sequence: 3 givenname: Danlu surname: Wang fullname: Wang, Danlu organization: School of Mathematics and Statistics, Wuhan University – sequence: 4 givenname: Jing surname: Zhang fullname: Zhang, Jing email: jing66@zuel.edu.cn organization: School of Statistics and Mathematics, Zhongnan University of Economics and Law
BookMark	eNp9kM1OAjEUhRuDiYA-gLsmrqv9YdqZJUFRE9TEwLop7R0YMkyxHWJw5cI34A15EoeMiStd3bP4zrnJ10OdyleA0CWj14xSdRMZZWxAKE-I4FwSfoK6bCAyoiRTnZ-cJkyeoV6MK0qTJKOyixZD_Azvta_IdLcB_AT10juc-4APX3tKXmGxLU0oPsDhobVQQjB1k8emKLcB8LRYNyXvoMSzykHA9RLwyEQ4fO5HfulDjW8hFovqHJ3mpoxw8XP7aDa-m44eyOTl_nE0nBDLs7Qm3CojMum4U86oOR3kqRRpIiWjwBJFrbRMWulSmksDhoJzdpA2wDwTigkr-uiq3d0E_7aFWOuV34aqeakFV4nIOKf8f0pmksukYfuItZQNPsYAud6EYm3CTjOqj9Z1a1031vXRuj4u87YTG7ZaQPhd_rv0DXNvhjc
Cites_doi	10.1002/sim.4780070116 10.1093/biostatistics/kxs025 10.1093/biomet/86.4.755 10.1111/j.1541-0420.2006.00562.x 10.1073/pnas.0932692100 10.1214/aos/1176349016 10.1093/biomet/93.4.747 10.1002/sim.2576 10.1016/j.csda.2022.107430 10.1007/s10463-006-0086-0 10.1111/j.1541-0420.2008.01074.x 10.1016/j.spl.2020.108925 10.1111/sjos.12503 10.1111/j.1467-9868.2008.00674.x 10.1007/s11222-015-9555-8 10.1111/j.1467-9469.2010.00701.x 10.1093/biomet/66.3.429 10.1002/sim.6623 10.1111/j.1467-9868.2008.00639.x 10.1214/aos/1176349273 10.1023/A:1009691327335 10.1111/j.1541-0420.2008.01055.x 10.1214/13-AOS1159 10.1093/biomet/91.2.305 10.1007/s10985-009-9144-2 10.1198/016214508000000184 10.1515/9783112479926-011 10.1007/BF01581275 10.1214/10-AOAS388 10.1007/s11222-013-9388-2 10.1093/biomet/90.2.341 10.1016/j.jspi.2009.04.023 10.1137/1.9780898718614 10.1016/j.jmva.2013.07.011 10.1093/biomet/73.1.1 10.1016/j.jkss.2016.10.006 10.1007/s10985-017-9402-7
ContentType	Journal Article
Copyright	Springer-Verlag GmbH Germany & The Editorial Office of AMS 2025 Springer-Verlag GmbH Germany & The Editorial Office of AMS 2025.
Copyright_xml	– notice: Springer-Verlag GmbH Germany & The Editorial Office of AMS 2025 – notice: Springer-Verlag GmbH Germany & The Editorial Office of AMS 2025.
DBID	AAYXX CITATION 7SC 7TB 8FD FR3 JQ2 KR7 L7M L~C L~D
DOI	10.1007/s10114-025-3226-2
DatabaseName	CrossRef Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Civil Engineering Abstracts Civil Engineering Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Mathematics
EISSN	1439-7617
EndPage	2300
ExternalDocumentID	10_1007_s10114_025_3226_2
GroupedDBID	-Y2 .86 .VR 06D 0R~ 0VY 1N0 1SB 2.D 23M 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 40D 40E 5GY 5VR 5VS 67Z 6NX 7WY 88I 8FE 8FG 8FL 8G5 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO ABAKF ABBBX ABBRH ABDBE ABDBF ABDZT ABECU ABFTV ABHLI ABHQN ABJCF ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADHKG ADIMF ADKNI ADKPE ADQRH ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFDZB AFEXP AFFHD AFGCZ AFKRA AFLOW AFOHR AFQWF AFUIB AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMVHM AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG ATHPR AVWKF AXYYD AZFZN AZQEC B-. B0M BA0 BAPOH BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCEZO CCPQU CCVFK CHBEP COF CS3 CSCUP CW9 DDRTE DL5 DNIVK DPUIP DWQXO EAD EAP EAS EBLON EBS EIOEI EJD EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ7 GUQSH H13 HCIFZ HF~ HG6 HMJXF HRMNR HVGLF HZ~ IHE IJ- IKXTQ ITM IWAJR IXC IZQ I~X I~Z J-C JBSCW JZLTJ K60 K6V K6~ K7- KDC KOV L6V LAS LLZTM M0C M2O M2P M4Y M7S MA- N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J P19 P62 P9R PF0 PHGZM PHGZT PQBIZ PQBZA PQGLB PQQKQ PROAC PT4 PT5 PTHSS Q2X QOS R4E R89 R9I ROL RPX RSV S16 S1Z S26 S27 S28 S3B SAP SCL SCLPG SDH SHX SISQX SJYHP SMT SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TGP TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR ZMTXR ZWQNP ~8M ~A9 AAYXX CITATION 7SC 7TB 8FD FR3 JQ2 KR7 L7M L~C L~D
ID	FETCH-LOGICAL-c298t-2c7a396d2d7da7b04f863856610e1570c6c16c6d80f6aea0eddc48856b93713c3
IEDL.DBID	RSV
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001609737100007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1439-8516
IngestDate	Thu Nov 27 04:23:19 EST 2025 Sat Nov 08 05:26:57 EST 2025 Sat Nov 29 06:52:12 EST 2025 Fri Nov 07 01:13:15 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	9
Keywords	62N01 62P10 62N02 newton-type method regularization case–cohort design 62D99 Accelerated failure time model support detection and root finding algorithm
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c298t-2c7a396d2d7da7b04f863856610e1570c6c16c6d80f6aea0eddc48856b93713c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
PQID	3269626575
PQPubID	54875
PageCount	26
ParticipantIDs	proquest_journals_3275392202 proquest_journals_3269626575 crossref_primary_10_1007_s10114_025_3226_2 springer_journals_10_1007_s10114_025_3226_2
PublicationCentury	2000
PublicationDate	2025-09-01
PublicationDateYYYYMMDD	2025-09-01
PublicationDate_xml	– month: 09 year: 2025 text: 2025-09-01 day: 01
PublicationDecade	2020
PublicationPlace	Berlin/Heidelberg
PublicationPlace_xml	– name: Berlin/Heidelberg – name: Heidelberg
PublicationTitle	Acta mathematica Sinica. English series
PublicationTitleAbbrev	Acta. Math. Sin.-English Ser
PublicationYear	2025
Publisher	Springer Berlin Heidelberg Springer Nature B.V
Publisher_xml	– name: Springer Berlin Heidelberg – name: Springer Nature B.V
References	B M Brown (3226_CR2) 2007; 26 J Huang (3226_CR12) 2010; 16 T Sørlie (3226_CR31) 2003; 100 W Stute (3226_CR33) 1993; 21 Y H Chen (3226_CR7) 2009; 139 B Kummer (3226_CR26) 1988; 45 S H Chiou (3226_CR9) 2014; 24 A Ni (3226_CR27) 2018; 24 W Stute (3226_CR32) 1996; 23 T Cai (3226_CR4) 2009; 65 M Yu (3226_CR37) 2011; 38 J Huang (3226_CR14) 2018; 19 K Chen (3226_CR6) 1999; 86 J Huang (3226_CR15) 2021; 168 J Fan (3226_CR10) 2008; 70 Z L Ying (3226_CR36) 1993; 21 L Wang (3226_CR35) 2013; 41 L Q Qi (3226_CR30) 1993; 58 Q Q Yu (3226_CR38) 2007; 59 P Breheny (3226_CR1) 2011; 5 J Huang (3226_CR13) 2006; 62 S Kim (3226_CR23) 2016; 35 J W Hu (3226_CR11) 2013; 122 L Kong (3226_CR25) 2009; 65 B A Johnson (3226_CR18) 2008; 70 Z Z Jin (3226_CR17) 2003; 90 J D Kalbfleisch (3226_CR20) 1988; 7 T M Therneau (3226_CR34) 1999; 5 L Kong (3226_CR24) 2004; 91 J Buckley (3226_CR3) 1979; 66 K Ito (3226_CR16) 2008 B Nan (3226_CR28) 2006; 93 C Cheng (3226_CR8) 2022; 170 S Kang (3226_CR21) 2013; 14 R L Prentice (3226_CR29) 1986; 73 J Zhang (3226_CR39) 2021; 48 Y X Cao (3226_CR5) 2017; 46 M H R Khan (3226_CR22) 2016; 26 B A Johnson (3226_CR19) 2008; 103
References_xml	– volume: 19 start-page: 403 year: 2018 ident: 3226_CR14 publication-title: J. Mach. Learn. Res. – volume: 7 start-page: 149 year: 1988 ident: 3226_CR20 publication-title: Stat. Med. doi: 10.1002/sim.4780070116 – volume: 14 start-page: 28 year: 2013 ident: 3226_CR21 publication-title: Biostatistics doi: 10.1093/biostatistics/kxs025 – volume: 86 start-page: 755 year: 1999 ident: 3226_CR6 publication-title: Biometrika doi: 10.1093/biomet/86.4.755 – volume: 62 start-page: 813 year: 2006 ident: 3226_CR13 publication-title: Biometrics doi: 10.1111/j.1541-0420.2006.00562.x – volume: 100 start-page: 8418 year: 2003 ident: 3226_CR31 publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0932692100 – volume: 21 start-page: 76 year: 1993 ident: 3226_CR36 publication-title: Ann. Statist. doi: 10.1214/aos/1176349016 – volume: 93 start-page: 747 year: 2006 ident: 3226_CR28 publication-title: Biometrika doi: 10.1093/biomet/93.4.747 – volume: 26 start-page: 828 year: 2007 ident: 3226_CR2 publication-title: Stat. Med. doi: 10.1002/sim.2576 – volume: 170 start-page: 107430 year: 2022 ident: 3226_CR8 publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2022.107430 – volume: 59 start-page: 675 year: 2007 ident: 3226_CR38 publication-title: Ann. Inst. Statist. Math. doi: 10.1007/s10463-006-0086-0 – volume: 23 start-page: 461 year: 1996 ident: 3226_CR32 publication-title: Scand. J. Stat. – volume: 65 start-page: 394 year: 2009 ident: 3226_CR4 publication-title: Biometrics doi: 10.1111/j.1541-0420.2008.01074.x – volume: 168 start-page: 108925 year: 2021 ident: 3226_CR15 publication-title: Statist. Probab. Lett. doi: 10.1016/j.spl.2020.108925 – volume: 48 start-page: 349 year: 2021 ident: 3226_CR39 publication-title: Scand. J. Stat. doi: 10.1111/sjos.12503 – volume: 70 start-page: 849 issue: 5 year: 2008 ident: 3226_CR10 publication-title: J. R. Stat. Soc. Ser. B doi: 10.1111/j.1467-9868.2008.00674.x – volume: 26 start-page: 725 year: 2016 ident: 3226_CR22 publication-title: Stat. Comput. doi: 10.1007/s11222-015-9555-8 – volume: 38 start-page: 252 year: 2011 ident: 3226_CR37 publication-title: Scand. J. Stat. doi: 10.1111/j.1467-9469.2010.00701.x – volume: 66 start-page: 429 year: 1979 ident: 3226_CR3 publication-title: Biometrika doi: 10.1093/biomet/66.3.429 – volume: 35 start-page: 282 year: 2016 ident: 3226_CR23 publication-title: Stat. Med. doi: 10.1002/sim.6623 – volume: 70 start-page: 351 year: 2008 ident: 3226_CR18 publication-title: J. R. Stat. Soc. B doi: 10.1111/j.1467-9868.2008.00639.x – volume: 21 start-page: 1591 year: 1993 ident: 3226_CR33 publication-title: Ann. Statist. doi: 10.1214/aos/1176349273 – volume: 5 start-page: 99 year: 1999 ident: 3226_CR34 publication-title: Lifetime Data Anal. doi: 10.1023/A:1009691327335 – volume: 65 start-page: 135 year: 2009 ident: 3226_CR25 publication-title: Biometrics doi: 10.1111/j.1541-0420.2008.01055.x – volume: 41 start-page: 2505 year: 2013 ident: 3226_CR35 publication-title: Ann. Stat. doi: 10.1214/13-AOS1159 – volume: 91 start-page: 305 year: 2004 ident: 3226_CR24 publication-title: Biometrika doi: 10.1093/biomet/91.2.305 – volume: 16 start-page: 176 issue: 2 year: 2010 ident: 3226_CR12 publication-title: Lifetime Data Anal. doi: 10.1007/s10985-009-9144-2 – volume: 103 start-page: 672 issue: 482 year: 2008 ident: 3226_CR19 publication-title: J. Amer. Statist. Assoc. doi: 10.1198/016214508000000184 – volume: 45 start-page: 114 year: 1988 ident: 3226_CR26 publication-title: Adv. Math. Optim. doi: 10.1515/9783112479926-011 – volume: 58 start-page: 353 year: 1993 ident: 3226_CR30 publication-title: Math. Program. doi: 10.1007/BF01581275 – volume: 5 start-page: 232 year: 2011 ident: 3226_CR1 publication-title: Ann. Appl. Stat. doi: 10.1214/10-AOAS388 – volume: 24 start-page: 559 year: 2014 ident: 3226_CR9 publication-title: Stat. Comput. doi: 10.1007/s11222-013-9388-2 – volume: 90 start-page: 341 year: 2003 ident: 3226_CR17 publication-title: Biometrika doi: 10.1093/biomet/90.2.341 – volume: 139 start-page: 3706 year: 2009 ident: 3226_CR7 publication-title: J. Stat. Plan. Inference doi: 10.1016/j.jspi.2009.04.023 – volume-title: Lagrange Multiplier Approach to Variational Problems and Applications year: 2008 ident: 3226_CR16 doi: 10.1137/1.9780898718614 – volume: 122 start-page: 96 year: 2013 ident: 3226_CR11 publication-title: J. Multivariate Anal. doi: 10.1016/j.jmva.2013.07.011 – volume: 73 start-page: 1 year: 1986 ident: 3226_CR29 publication-title: Biometrika doi: 10.1093/biomet/73.1.1 – volume: 46 start-page: 298 year: 2017 ident: 3226_CR5 publication-title: J. Korean Stat. Soc. doi: 10.1016/j.jkss.2016.10.006 – volume: 24 start-page: 443 year: 2018 ident: 3226_CR27 publication-title: Lifetime Data Anal. doi: 10.1007/s10985-017-9402-7
SSID	ssj0055906
Score	2.3705642
Snippet	The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies. In this paper, we study the high-dimensional...
SourceID	proquest crossref springer
SourceType	Aggregation Database Index Database Publisher
StartPage	2275
SubjectTerms	Algorithms Failure times Gene expression Least squares method Mathematics Mathematics and Statistics Newton methods Parameter estimation Regularization Weighting functions
Title	A Newton-Type Method for ℓ0-Regularized Accelerated Failure Time Model Under the Case–Cohort Design
URI	https://link.springer.com/article/10.1007/s10114-025-3226-2 https://www.proquest.com/docview/3269626575 https://www.proquest.com/docview/3275392202
Volume	41
WOSCitedRecordID	wos001609737100007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1439-7617 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055906 issn: 1439-8516 databaseCode: RSV dateStart: 19990101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwELZQYYCBN6JQkAcmkCXXaZxkrAoVA61QgapblPgBlVCLmsLAxMA_6D_sL-HOTSggOsAWybET3dl3n3WPj5AT3wDI1zJh0qYeqwVWsxRZU8F5Cu6L1Pqub0H3Kmi3w14vus7ruLMi270ISTpL_aXYDbA7w4VgE0oGdncZe5cgbUHnpluYX0DIfFZS5EUM4IQsQpm_LfHdGc0R5o-gqPM1zY1__eUmWc-hJa3P9sIWWTKDbbLW-uzLmu2Q-zoFq4aswXj_pC1HH00Bt9Lp-4SzjiOmH_VfjaZ1pcAjYSMJTZtJH7PXKdaLUGRPe6SOL4nC0rQBfnD6NmkMHwDJ03OXEbJL7poXt41LllMtMCWicMyEChIvklroQCdByms2hIMJUK_KTdUPuJKqKpXUIbcyMQk3Wis4-r5MsZ-ep7w9UhoMB2af0NSTktvAT01owSDYNLH4ZMH2GqF4VCanhczjp1lHjXjeOxmlF4P0YpReLMqkUmglzg9XBkMS1I4RowXDcAWLhOAw-6xQ0nx44bcO_vT2IVkVqGWXblYhpfHo2RyRFfUy7mejY7clPwBQL9sL
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwELYQIAEDb0ShgAcmkCXHaZxkrApVEW2FSqm6RYkfUAm1qCkMTAz8g_7D_hLObkIB0QG2SI6d6Hy--6x7fAidegpAvuQx4TpxScnXkiSGNRWcJ6MeS7Rn-xZ06n6zGXS74U1Wx53m2e55SNJa6i_FboDdiVkIlJATsLtLJea4Rq1bt53c_AJCptOSIjckACd4Hsr8bYnvzmiGMH8ERa2vqW786y830XoGLXF5qgtbaEH1t9Fa47Mva7qD7ssYrJphDTb3T9yw9NEYcCuevI8paVli-mHvVUlcFgI8kmkkIXE17pnsdWzqRbBhT3vEli8Jw9K4An5w8jauDB4AyeMLmxGyi-6ql-1KjWRUC0SwMBgRJvzYDblk0pexn9CSDuBgAtRzqHI8nwouHC64DKjmsYqpklLA0fd4YvrpucLdQ4v9QV_tI5y4nFPte4kKNBgEncTaPGmwvYoJGhbQWS7z6GnaUSOa9U420otAepGRXsQKqJjvSpQdrhSGeAj3MACac4bhChYyRmH2eb5Js-G53zr409snaKXWbtSj-lXz-hCtMrPjNvWsiBZHw2d1hJbFy6iXDo-ten4AnWjd7w
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEF5ERfTgW6xW3YMnZel202ySY2kNim0pPkpvIdmHFqQtTfXgyYP_oP-wv8TZPKyKPYi3wCabMLuZ-Yad-T6ETm0FIF_ykHAdWaTiaEkio5oKwZNRm0XaTngLOg2n1XK7Xa-d6ZzGebV7fiSZ9jQYlqb-uDSUuvSl8Q1wPDGTwobkBHzwUsWQoZl0_baTu2JAyzRtL7I8AtCC58eav03xPTDN0OaPA9Ik7vgb__7iTbSeQU5cTffIFlpQ_W201vzka4130EMVg7czasImL8XNRFYaA57F0_cJJTeJYP2o96okrgoBkcoQTEjshz1T1Y5NHwk2qmpPONFRwjA1rkF8nL5NaoNHQPi4nlSK7KJ7_-KudkkyCQYimOeOCRNOaHlcMunI0IloRbvwwwIELFNVth0quChzwaVLNQ9VSJWUAlyCzSPDs2cJaw8t9gd9tY9wZHFOtWNHytXgKHQUanOlwScrJqhXQGe5_YNhyrQRzDiVjfUCsF5grBewAirmKxRkP10MQ9yD_AwA6JxhSM08xig8fZ4v2Gx47rsO_nT3CVpp1_2gcdW6PkSrzCx4UpFWRIvj0bM6QsviZdyLR8fJTv0Ar5fm0w
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Newton-Type+Method+for+%E2%84%930-Regularized+Accelerated+Failure+Time+Model+Under+the+Case%E2%80%93Cohort+Design&rft.jtitle=Acta+mathematica+Sinica.+English+series&rft.au=Liu%2C+Yanyan&rft.au=Tian%2C+Ke&rft.au=Wang%2C+Danlu&rft.au=Zhang%2C+Jing&rft.date=2025-09-01&rft.pub=Springer+Berlin+Heidelberg&rft.issn=1439-8516&rft.eissn=1439-7617&rft.volume=41&rft.issue=9&rft.spage=2275&rft.epage=2300&rft_id=info:doi/10.1007%2Fs10114-025-3226-2&rft.externalDocID=10_1007_s10114_025_3226_2
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1439-8516&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1439-8516&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1439-8516&client=summon