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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Semnan University</PublisherName>
				<JournalTitle>International Journal of Nonlinear Analysis and Applications</JournalTitle>
				<Issn>2008-6822</Issn>
				<Volume>13</Volume>
				<Issue>2</Issue>
				<PubDate PubStatus="epublish">
					<Year>2022</Year>
					<Month>07</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Discrete alpha-power Weibull distribution: Properties and application</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1305</FirstPage>
			<LastPage>1317</LastPage>
			<ELocationID EIdType="pii">6301</ELocationID>
			
<ELocationID EIdType="doi">10.22075/ijnaa.2022.6301</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>M. O.</FirstName>
					<LastName>Mohamed</LastName>
<Affiliation>Faculty of Science, Mathematics Department, Zagazig University, Zagazig, Egypt</Affiliation>

</Author>
<Author>
					<FirstName>N. A.</FirstName>
					<LastName>Hassan</LastName>
<Affiliation>Faculty of Science, Mathematics Department, Zagazig University, Zagazig, Egypt</Affiliation>

</Author>
<Author>
					<FirstName>Nahla</FirstName>
					<LastName>Abdelrahman</LastName>
<Affiliation>Faculty of Science, Mathematics Department, Zagazig University, Zagazig, Egypt</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2022</Year>
					<Month>02</Month>
					<Day>02</Day>
				</PubDate>
			</History>
		<Abstract>A three-parameter discrete analogue of the Alpha-power Weibull distribution (DAPW) is provided in this study. It has established some of its basic distributional and statistical properties. The probability mass function&#039;s form, moments, skewness, kurtosis, probability generating function, characteristic function, stress-strength reliability, and order statistics are all examples of this. The unknown parameters are estimated using the maximum likelihood and moments approaches. The bias and mean square error of the maximum likelihood are demonstrated via a simulated exercise. Two datasets are used to demonstrate the model&#039;s adaptability.</Abstract>
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			<Object Type="keyword">
			<Param Name="value">characterization</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Maximum likelihood estimator</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">survival function</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Quantile</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Reliability</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Failure rate</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Second rate of failure</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://ijnaa.semnan.ac.ir/article_6301_407f177d69f5b898bb991d5acab98482.pdf</ArchiveCopySource>
</Article>
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