View in EDS PDF Full Text

LIC for Distributed Censored Regression.

Saved in:
Bibliographic Details
Title: LIC for Distributed Censored Regression.
Authors: Zhang, Chuanqi1 Zhangcq0601@163.com, Guo, Guangbao2 ggb1111111@163.com
Source: IAENG International Journal of Applied Mathematics. Jul2026, Vol. 56 Issue 7, p2603-2611. 9p.
Subjects: Distributed computing, Censoring (Statistics), Big data, Robust statistics
Abstract: We introduce a novel distributed censored regression that combines the flexibility of censored distributions with the efficiency of distributed computing, effectively addressing the challenges associated with large-scale censored datasets. Within this framework, we propose an optimal subset selection criterion named LIC. Comparative analysis with two widely used metrics demonstrates that LIC achieves superior stability and sensitivity in reducing estimation errors. In addition, we evaluate the applicability of the LIC to various distributed censored regression models, with experimental data further corroborating its robustness and stability. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 195026893
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: LIC for Distributed Censored Regression.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Chuanqi%22">Zhang, Chuanqi</searchLink><relatesTo>1</relatesTo><i> Zhangcq0601@163.com</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Guangbao%22">Guo, Guangbao</searchLink><relatesTo>2</relatesTo><i> ggb1111111@163.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Jul2026, Vol. 56 Issue 7, p2603-2611. 9p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Censoring+%28Statistics%29%22">Censoring (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We introduce a novel distributed censored regression that combines the flexibility of censored distributions with the efficiency of distributed computing, effectively addressing the challenges associated with large-scale censored datasets. Within this framework, we propose an optimal subset selection criterion named LIC. Comparative analysis with two widely used metrics demonstrates that LIC achieves superior stability and sensitivity in reducing estimation errors. In addition, we evaluate the applicability of the LIC to various distributed censored regression models, with experimental data further corroborating its robustness and stability. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195026893
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 2603
    Subjects:
      – SubjectFull: Distributed computing
        Type: general
      – SubjectFull: Censoring (Statistics)
        Type: general
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Robust statistics
        Type: general
    Titles:
      – TitleFull: LIC for Distributed Censored Regression.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhang, Chuanqi
      – PersonEntity:
          Name:
            NameFull: Guo, Guangbao
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19929978
          Numbering:
            – Type: volume
              Value: 56
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: IAENG International Journal of Applied Mathematics
              Type: main
ResultId 1