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<Article>
<Journal>
				<PublisherName>Semnan University</PublisherName>
				<JournalTitle>International Journal of Nonlinear Analysis and Applications</JournalTitle>
				<Issn>2008-6822</Issn>
				<Volume>12</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2021</Year>
					<Month>05</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Some estimation procedures of the PDF and CDF of the generalized inverted Weibull distribution with comparison</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1017</FirstPage>
			<LastPage>1036</LastPage>
			<ELocationID EIdType="pii">4957</ELocationID>
			
<ELocationID EIdType="doi">10.22075/ijnaa.2020.21419.2257</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Mortaza</FirstName>
					<LastName>Ghasemi Cherati</LastName>
<Affiliation>Department of Statistics, Qaemshahr Branch, IslamicAzad University, Qaemshahr, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Ezzatallah</FirstName>
					<LastName>Baloui Jamkhaneh</LastName>
<Affiliation>Department of Statistics, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Einolah</FirstName>
					<LastName>Deiri</LastName>
<Affiliation>Department of Statistics, Qaemshahr Branch, IslamicAzad University, Qaemshahr, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2020</Year>
					<Month>09</Month>
					<Day>03</Day>
				</PubDate>
			</History>
		<Abstract>Different estimation procedures for the probability density and cumulative distribution functions of the generalized inverted Weibull distribution are discussed. For this purpose, the parametric and non-parametric estimation approaches as maximum likelihood, uniformly minimum variance unbiased, percentile, least squares and weighted least squares estimators are considered and compared. The expectations and mean square error of the maximum likelihood and uniformly minimum variance unbiased estimation are provided in the closed-form whereas, for non-parametric estimation methods (percentile, least squares and weighted least squares), the expectations and mean square error are computed via the simulation data. The Monte Carlo simulations are provided to assess the performances of the proposed estimation methods. Finally, the analysis of the real data set has been presented for illustrative purposes.</Abstract>
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			<Object Type="keyword">
			<Param Name="value">Generalized inverted Weibull distribution</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Maximum likelihood estimator</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Uniformly minimum variance unbiased estimator</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Percentile estimator</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Least squares estimator</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Weighted least squares estimator</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://ijnaa.semnan.ac.ir/article_4957_10dfb4530f479587790b1f76f96f0f63.pdf</ArchiveCopySource>
</Article>
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