Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring

Sultana, Farha; Tripathi, Yogesh Mani; Wu, Shuo‐Jye; Sen, Tanmay

doi:10.1007/s40745-020-00283-z

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…In this subsection, we examine actual data that pertains to the Shasta reservoir's monthly water capacity in California, USA. The data covers the month of February between 1991 and 2010. see Sultana et al 25 and Sultana et al 12 . The data points are listed below as follows.…”

Section: Simulation Study and Real Data Analysismentioning

confidence: 99%

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Optimal sampling and statistical inferences for Kumaraswamy distribution under progressive Type-II censoring schemes

Abo-Kasem

Saeed

Sayed

2023

Sci Rep

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In this paper, we study non-Bayesian and Bayesian estimation of parameters for the Kumaraswamy distribution based on progressive Type-II censoring. First, the maximum likelihood estimates and maximum product spacings are derived. In addition, we derive the asymptotic distribution of the parameters and the asymptotic confidence intervals. Second, Bayesian estimators under symmetric and asymmetric loss functions (Squared error, linear exponential, and general entropy loss functions) are also obtained. The Lindley approximation and the Markov chain Monte Carlo method are used to derive the Bayesian estimates. Furthermore, we derive the highest posterior density credible intervals of the parameters. We further present an optimal progressive censoring scheme among different competing censoring scheme using three optimality criteria. Simulation studies are conducted to evaluate the performance of the point and interval estimators. Finally, one application of real data sets is provided to illustrate the proposed procedures.

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Section: Simulation Study and Real Data Analysismentioning

confidence: 99%

“…The classical Bayes estimates were obtained by utilizing the Markov chain Monte Carlo technique. Sultana et al 12 investigated the estimation problems of unknown parameters of the Kumaraswamy distribution under Type-I progressive hybrid censoring. They derived the maximum likelihood estimates of parameters using an EM algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimal sampling and statistical inferences for Kumaraswamy distribution under progressive Type-II censoring schemes

Abo-Kasem

Saeed

Sayed

2023

Sci Rep

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“…Then, the authors in Ref. [10] continued to focus on this distribution under hybrid progressive type I censored samples. The authors in Ref.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for the Parameters of Kumaraswamy Distribution via Ranked Set Sampling

Jiang

Gui

2021

Symmetry

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In this paper, we address the estimation of the parameters for a two-parameter Kumaraswamy distribution by using the maximum likelihood and Bayesian methods based on simple random sampling, ranked set sampling, and maximum ranked set sampling with unequal samples. The Bayes loss functions used are symmetric and asymmetric. The Metropolis-Hastings-within-Gibbs algorithm was employed to calculate the Bayes point estimates and credible intervals. We illustrate a simulation experiment to compare the implications of the proposed point estimators in sense of bias, estimated risk, and relative efficiency as well as evaluate the interval estimators in terms of average confidence interval length and coverage percentage. Finally, a real-life example and remarks are presented.

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“…The authors proposed various estimates. One may refer to Kayal et al (2019), Goyal et al (2020) and Sultana et al (2020) and the references therein for some more references in this topic.…”

Section: Introductionmentioning

confidence: 99%

Estimation of parameters of the logistic exponential distribution under progressive type-I hybrid censored sample

Dutta¹,

Kayal²

2021

Preprint

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The paper addresses the problem of estimation of the model parameters of the logistic exponential distribution based on progressive type-I hybrid censored sample. The maximum likelihood estimates are obtained and computed numerically using Newton-Raphson method. Further, the Bayes estimates are derived under squared error, LINEX and generalized entropy loss functions. Two types (independent and bivariate) of prior distributions are considered for the purpose of Bayesian estimation. It is seen that the Bayes estimates are not of explicit forms. Thus, Lindley's approximation technique is employed to get approximate Bayes estimates. Interval estimates of the parameters based on normal approximate of the maximum likelihood estimates and normal approximation of the log-transformed maximum likelihood estimates are constructed. The highest posterior density credible intervals are obtained by using the importance sampling method. Furthermore, numerical computations are reported to review some of the results obtained in the paper. A real life dataset is considered for the purpose of illustrations.

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Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring

Cited by 10 publications

References 39 publications

Optimal sampling and statistical inferences for Kumaraswamy distribution under progressive Type-II censoring schemes

Optimal sampling and statistical inferences for Kumaraswamy distribution under progressive Type-II censoring schemes

Bayesian Inference for the Parameters of Kumaraswamy Distribution via Ranked Set Sampling

Estimation of parameters of the logistic exponential distribution under progressive type-I hybrid censored sample

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