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<Article>
<Journal>
				<PublisherName>University of Kashan</PublisherName>
				<JournalTitle>Mathematics Interdisciplinary Research</JournalTitle>
				<Issn>2538-3639</Issn>
				<Volume>11</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2026</Year>
					<Month>09</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Some Characteristics of Tensor-Variate t and Skew-t Distributions</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>299</FirstPage>
			<LastPage>319</LastPage>
			<ELocationID EIdType="pii">115686</ELocationID>
			
<ELocationID EIdType="doi">10.22052/mir.2026.257883.1553</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Fatemeh</FirstName>
					<LastName>Yousefzadeh</LastName>
<Affiliation>‎Department of Statistics, ‎University of Birjand, ‎Birjand‎, ‎I‎. ‎R‎. ‎Iran</Affiliation>

</Author>
<Author>
					<FirstName>Seyed Ali Asgar</FirstName>
					<LastName>Tajadod</LastName>
<Affiliation>‎Department of Statistics, ‎University of Birjand, ‎Birjand‎, ‎I‎. ‎R‎. ‎Iran</Affiliation>

</Author>
<Author>
					<FirstName>Raynaldo Boris</FirstName>
					<LastName>Arellano-Valle</LastName>
<Affiliation>‎Department of Estadistica,
 ‎Pontificia Universidad Catolica de Chile, Santiago‎, ‎Chile</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2025</Year>
					<Month>11</Month>
					<Day>01</Day>
				</PubDate>
			</History>
		<Abstract>‎In recent years‎, ‎rapid technological advances have led to the emergence of increasingly complex data structures‎, ‎among which tensor data play a central role‎. ‎To address the challenges of analyzing such data‎, ‎it is essential to extend statistical concepts and methodologies to the tensor domain‎. ‎In this paper‎, ‎we introduce the tensor-variate t‎ and skew-t distributions and derive several of their statistical properties‎. ‎Parameter estimation is carried out using the Expectation-Maximization (EM) algorithm‎, ‎and the performance of the proposed estimators is assessed through extensive simulation studies‎. ‎Finally‎, ‎the effectiveness and practical applicability of the estimators are demonstrated using real world data‎.</Abstract>
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			<Object Type="keyword">
			<Param Name="value">Kronecker-separable covariance‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">‎Multidimensional array‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">‎Tensor‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">‎EM algorithm‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">‎Image learning</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://mir.kashanu.ac.ir/article_115686_fd45f9f4205abeff1d66b8e38e20d93a.pdf</ArchiveCopySource>
</Article>
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