Parameter estimation for the Kumaraswamy distribution based on hybrid censoring

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/114923

Title:	Parameter estimation for the Kumaraswamy distribution based on hybrid censoring
Authors:	Farha Sultana;Yogesh Mani Tripathi;Manoj Kumar Rastogi;Shuo-Jye Wu
Keywords:	Bayes estimates;EM algorithm;importance sampling;Lindley method;Tierney and Kadane method
Date:	2018-05-09
Issue Date:	2018-09-18 12:11:02 (UTC+8)
Abstract:	We consider estimation of unknown parameters of a two-parameter Kumaraswamy distribution with hybrid censored samples. We obtain maximum likelihood estimates using an expectation-maximization algorithm. Bayes estimates are derived under the squared error loss function using different approximation methods. In addition, an importance sampling technique is also discussed. Interval estimation is considered as well. We conduct a simulation study to compare the performance of different estimates, and based on this study, recommendations are made. A real data set and a simulated data set are analyzed for illustration purposes.
Relation:	American Journal of Mathematical and Management Sciences 37(3), p.243-261
DOI:	10.1080/01966324.2017.1396943
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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