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<ArticleSet>
<Article>
<Journal>
<PublisherName>OICC Press</PublisherName>
<JournalTitle>International Journal of Mathematical Modelling &amp; Computations</JournalTitle>
<Issn>2228-6233</Issn>
<Volume>13</Volume>
<Issue>4</Issue>
<PubDate PubStatus="epublish">
<Year>2024</Year>
<Month>11</Month>
<Day>15</Day>
</PubDate>
</Journal>
<ArticleTitle>Estimation of Multi-Component Reliability Parameter in a Non-identical-Component Strengths System Under Dependency of Stress and Strength Components</ArticleTitle>
<VernacularTitle></VernacularTitle>
<FirstPage></FirstPage>
<LastPage></LastPage>
<ELocationID EIdType="doi">10.71932/ijm.2023.1081413</ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>Akram</FirstName>
<LastName>Kohansal</LastName>
<Affiliation>Imam Khomeini International University, Iran</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
</AuthorList>
<PublicationType>Journal Article</PublicationType>
<History>
<PubDate PubStatus="received">
<Year>2024</Year>
<Month>11</Month>
<Day>15</Day>
</PubDate>
</History>
<Abstract>Generating more realistic stress-strength model is main attempt, in this paper. For this aim, inference on stress-strength parameter was considered in a multi-component system with the non-identical-component strengths, based on the Kumaraswamy generalized distribution, when the stress and strength variables are dependent. The dependency assumption is studied by Copula theory, one of the most important concept in dependent variables. The maximum likelihood estimation (MLE), bootstrap confidence interval, Bayesian approximation and highest posterior density (HPD) interval are obtained, for the multi-component stress-strength parameter. Employing Monte Carlo simulations, the performance of different estimations are compared together. Finally, one real data set is analyzed for illustrative purposes.</Abstract>
<ObjectList>
<Object Type="keyword">
<Param Name="value">Stress-strength reliability</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Kumaraswamy generalized distributions</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Copula theory</Param>
</Object>
<Object Type="keyword">
<Param Name="value">MCMC method</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Bayesian inference</Param>
</Object>
</ObjectList>
</Article>
</ArticleSet>