Parameter Estimation for the Kumaraswamy Distribution Based on Hybrid Censoring

Sultana, Farha; Tripathi, Yogesh Mani; Rastogi, Manoj Kumar; Wu, Shuo‐Jye

doi:10.1080/01966324.2017.1396943

Cited by 16 publications

(6 citation statements)

References 24 publications

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“…Maurya et al [ 28 ] derived the HPD credible intervals of unknown parameters in a Burr Type XII distribution using the importance sampling method. Sultana et al [ 29 ] considered the estimation of unknown parameters for two-parameter Kumaraswamy distribution with hybrid censored samples. In the subsection, we use the importance sampling method to obtain the HPD credible intervals of unknown parameters of the inverse power Lomax distribution.…”

Section: Bayesian Estimationmentioning

confidence: 99%

Inference for Inverse Power Lomax Distribution with Progressive First-Failure Censoring

Shi

2021

Entropy

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This paper investigates the statistical inference of inverse power Lomax distribution parameters under progressive first-failure censored samples. The maximum likelihood estimates (MLEs) and the asymptotic confidence intervals are derived based on the iterative procedure and asymptotic normality theory of MLEs, respectively. Bayesian estimates of the parameters under squared error loss and generalized entropy loss function are obtained using independent gamma priors. For Bayesian computation, Tierney–Kadane’s approximation method is used. In addition, the highest posterior credible intervals of the parameters are constructed based on the importance sampling procedure. A Monte Carlo simulation study is carried out to compare the behavior of various estimates developed in this paper. Finally, a real data set is analyzed for illustration purposes.

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Section: Bayesian Estimationmentioning

confidence: 99%

Inference for Inverse Power Lomax Distribution with Progressive First-Failure Censoring

Shi

2021

Entropy

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“…Singh and Tripathi (2015) analyzed a lognormal distribution and compared the classical and Bayesian results under HCS. Sultana et al (2018) discussed the Kumaraswamy distribution based on Type-I HCS. Pundir and Gupta (2018) analyzed a two-parameter Chen distribution to evaluate the MLE and Bayes estimate of P under HCS.…”

Section: Introductionmentioning

confidence: 99%

“…Singh and Tripathi (2015) analyzed a log-normal distribution and compared the classical and Bayesian results under HCS. Sultana et al . (2018) discussed the Kumaraswamy distribution based on Type-I HCS.…”

Section: Introductionmentioning

confidence: 99%

Estimation and prediction in generalized half logistic lifetime model using hybrid censored data

Soni

Shukla²,

Kumar

2023

IJQRM

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PurposeThis article aims to develop procedures for estimation and prediction in case of Type-I hybrid censored samples drawn from a two-parameter generalized half-logistic distribution (GHLD).Design/methodology/approachThe GHLD is a versatile model which is useful in lifetime modelling. Also, hybrid censoring is a time and cost-effective censoring scheme which is widely used in the literature. The authors derive the maximum likelihood estimates, the maximum product of spacing estimates and Bayes estimates with squared error loss function for the unknown parameters, reliability function and stress-strength reliability. The Bayesian estimation is performed under an informative prior set-up using the “importance sampling technique”. Afterwards, we discuss the Bayesian prediction problem under one and two-sample frameworks and obtain the predictive estimates and intervals with corresponding average interval lengths. Applications of the developed theory are illustrated with the help of two real data sets.FindingsThe performances of these estimates and prediction methods are examined under Type-I hybrid censoring scheme with different combinations of sample sizes and time points using Monte Carlo simulation techniques. The simulation results show that the developed estimates are quite satisfactory. Bayes estimates and predictive intervals estimate the reliability characteristics efficiently.Originality/valueThe proposed methodology may be used to estimate future observations when the available data are Type-I hybrid censored. This study would help in estimating and predicting the mission time as well as stress-strength reliability when the data are censored.

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“…[7] obtained the estimators of parameters for Kumaraswamy distribution based on the progressive type-II censoring data using Bayesian and non-Bayesian methods; the authors in Ref. [8] studied the estimations of parameters for this distribution under hybrid censoring; the authors in Ref. [9] ulteriorly discussed the estimations of the stress-strength parameters based on an incorporation of progressive type II and hybrid censoring schemes.…”

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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Parameter Estimation for the Kumaraswamy Distribution Based on Hybrid Censoring

Cited by 16 publications

References 24 publications

Inference for Inverse Power Lomax Distribution with Progressive First-Failure Censoring

Inference for Inverse Power Lomax Distribution with Progressive First-Failure Censoring

Estimation and prediction in generalized half logistic lifetime model using hybrid censored data

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

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