<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
<PublisherName>OICC Press</PublisherName>
<JournalTitle>International Journal of Mathematical Modelling &amp; Computations</JournalTitle>
<Issn>2228-6233</Issn>
<Volume>14</Volume>
<Issue>2</Issue>
<PubDate PubStatus="epublish">
<Year>2025</Year>
<Month>07</Month>
<Day>11</Day>
</PubDate>
</Journal>
<ArticleTitle>A New Compounded Model Based on Fre’chet Distribution With Application in Failure Data</ArticleTitle>
<VernacularTitle></VernacularTitle>
<FirstPage>129</FirstPage>
<LastPage>151</LastPage>
<ELocationID EIdType="doi">10.71932/IJM.2024.1128596</ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>Fereshteh</FirstName>
<LastName>Momeni</LastName>
<Affiliation>Department of Statistics, ‎Islamic Azad‎ ‎University‎, ‎ Behshahr Branch, ‎Iran</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>Kazem</FirstName>
<LastName>Fayyaz Heydari</LastName>
<Affiliation>Department of Statistics, Payame Noor University, ‎Tehran Branch‎, Iran.</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>Soheil</FirstName>
<LastName>Shokri</LastName>
<Affiliation>Department of Statistics, Rasht Branch, Islamic Azad University, Rasht, Iran</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
</AuthorList>
<PublicationType>Journal Article</PublicationType>
<History>
<PubDate PubStatus="received">
<Year>2025</Year>
<Month>07</Month>
<Day>11</Day>
</PubDate>
</History>
<Abstract>Recently the Extended Fre’chet distribution (EF) has appeard as the subject of so many research. In this research, we aim to extend EF as a three-parameter distribution to a four-parameter life-time distribution named Extended Frechet Power Series (EFPS) distribution. The EFPS distribution happens to have decreasing, increasing, bathtub, and upside down bathtub hazard shapes for different values of its parameters. The maximum likelihood estimation and capability of the quantile measures are discussed. Using two data sets leading to the numerical experiment, the functioning of the maximum likelihood estimators and their asymptotic results of EFPS distribution are compared to several rival destributions.</Abstract>
<ObjectList>
<Object Type="keyword">
<Param Name="value">Extended Frechet distribution</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Power series distribution</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Maximum likelihood estimation</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Moments</Param>
</Object>
<Object Type="keyword">
<Param Name="value">life-time distribution</Param>
</Object>
</ObjectList>
</Article>
</ArticleSet>