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  <doi_batch_id>aspg-21-3237-1791475459</doi_batch_id>
  <timestamp>20261008160419</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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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>2025</year>
    </publication_date>
    <journal_volume>
     <volume>25</volume>
    </journal_volume>
    <issue>3</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Compare Noise Robust Least-Squares Method with Other Methods for Estimation of the Parameters of Frechet Distribution and Neutrosophic Generalization</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>MAHA Adil</given_name>
      <surname>Abdulla</surname>
      <affiliations>
       <institution>
        <institution_name>The Post-graduate Institute for Accounting and Financial Studies</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Huda Hadib</given_name>
      <surname>Abbas</surname>
      <affiliations>
       <institution>
        <institution_name>The Post-graduate Institute for Accounting and Financial Studies</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The Frechet distribution is a versatile probability distribution that is used within a loose range in many important statistical fields, such as image processing, data analysis, and pattern recognition. It aims to explore and study the estimation of the parameters of the Frechet distribution using the noise-robust least squares method, as in the research paper, and it also has uses. There are many real-world scenarios. It is known that there is a growing challenge in estimating the parameter because of the noisy data. Depending on rigorous simulations and experimental analysis, we provide a novel powerful way to estimate the parameters for the Frechet Distribution Robust Least Squares approach to be flexible. Also, the results approach of this work will be very helpful in estimating the Frechet distribution parameters for diverse statistical applications. Also, we generalize our results to include the generalized neutrosophic case of this distribution dealing with neutrosophic numbers.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>01</first_page>
     <last_page>13</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3237</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/IJNS.250301</doi>
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