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  <doi_batch_id>aspg-21-1928-1791472342</doi_batch_id>
  <timestamp>20261008151222</timestamp>
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   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <journal>
   <journal_metadata language="en">
    <full_title>International Journal of Neutrosophic Science</full_title>
    <abbrev_title>IJNS</abbrev_title>
    <issn media_type="print">2692-6148</issn>
    <issn media_type="electronic">2690-6805</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <journal_volume>
     <volume>21</volume>
    </journal_volume>
    <issue>3</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Survival Function Estimation for Fuzzy Gompertz Distribution with neutrosophic data</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Alan Adham</given_name>
      <surname>Bibani</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Statistics and Informatics, University of Mosul, Mosul, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Zakariya Yahya</given_name>
      <surname>Algamal</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Statistics and Informatics, University of Mosul, Mosul, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>A relatively recent area of research known as neutrosophic statistics deals with data that are ambiguous, indeterminate, and inconsistent. By embracing the idea of neutrosophy, which denotes the existence of three components in a statement: truth, falsity, and indeterminacy, it broadens the application of classical statistics. One of the significant offshoots of statistics is life-time data analysis. Traditional statistical methods only account for variation within the data and calculate lifetime observations as accurate numbers. Actually, there are two different kinds of uncertainty in data: fluctuation between observations and fuzziness. As a result, analysis techniques that solely employ precise lifetime data and ignore fuzziness use incomplete information and produce false results. This paper sought to generalize hazard rates, survival functions, and parameter estimates for fuzzy Gompertz Distribution. Simulation studies are implemented to examine the performance of the fuzzy Gompertz Distribution. The results show that the fuzzy Gompertz Distribution has better flexibility in handling over the standard Gompertz Distribution.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>137</first_page>
     <last_page>142</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">1928</item_number>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/IJNS.210313</doi>
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