Estimation of parameters of Kumaraswamy-exponential distribution based on adaptive type-II progressive censored schemes

Mohan, Rakhi; Chacko, Manoj

doi:10.1080/00949655.2020.1807547

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…The data set represents the times of failures and running times for samples of devices from an eld-tracking study of a larger system. The data set was studied by Nelson (1998) Merovci & Elbatal (2014) Mohan & Chacko (2020) The data set has 30 observations and it is given below(see Oguntunde et al (2017) ) 2. 75, 0.13, 1.47, 0.23, 1.81, 0.30, 0.65, 0.10, 3.00, 1.73, 1.06, 3.00, 3.00, 2.12, 3.00, 3.00, 3.00, 0.02, 2.61, 2.93, 0.88, 2.47, 0.28, 1.43, 3.00, 0.23, 3.00, 0.80, 2.45, 2.66. We use Anderson-Darling(AD) test and Kolmogorov-Smirnov(KS) test for checking the goodness of fit.…”

Section: Real Data Analysismentioning

confidence: 99%

“…Panahi & Moradi (2020) discussed the problem of estimating parameters of the inverted exponentiated Rayleigh distribution based on AT-II PCS. Mohan & Chacko (2021) studied the estimation of parameters for a two parameter Kumaraswamy-exponential distribution based on AT-II PCS. An adaptive type-II progressive hybrid censored sampling with random removals is considered by Elshahhat & Nassar (2022).…”

Section: Introductionmentioning

confidence: 99%

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Statistical Inference and Optimal Design of Accelerated Life Testing for the Chen Distribution under Progressive Type-II Censoring

Zhang

Gui

2022

Mathematics

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This paper discusses statistical inference and optimal design of constant-stress accelerated life testing for the Chen distribution under progressive Type-II censoring. The scale parameter of the life distribution is assumed to be a logarithmic linear function of the stress level. The maximum likelihood estimates of the parameters are obtained. Then, the observed Fisher information matrix is derived and utilized to construct asymptotic confidence intervals. Meanwhile, the parametric bootstrap methods are provided for the interval estimation. In addition, the Bayes estimates under the squared error loss function are obtained by applying the Tierney and Kadane technique and Lindley’s approximation. As for the optimal design, D- and A-optimality criteria are considered to determine the optimal transformed stress level. Finally, the simulation is carried out to demonstrate the proposed estimation techniques and the optimal criteria, and a real data set is discussed.

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Section: Real Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Inference and Optimal Design of Accelerated Life Testing for the Chen Distribution under Progressive Type-II Censoring

Zhang

Gui

2022

Mathematics

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“…Ye et al (2014) [45] and Sobhi and Soliman (2016) [40] [19] and Sewailem (2019) [35] studied APT-II censored data for statistical analysis of Weibull exponential and log-logistic distribution respectively. Recently, Mohan and Chacko (2021) [30] considered Kumarswamy-exponential distribution under APT-II censoring scheme.…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

“…For n = 76, we consider m = 45 and make same censoring schemes as done in simulation [I = (0 14 , 1 31 ), II = (1, 0 14 , 1 30 ), III = (1, 2 2 , 0 16 , 1 26 )] with both test time T = 1.5 and T = 2.5 under APT-II censoring scheme. MLEs and the Bayes estimates are calculated under symmetric and asymmetric loss functions.…”

Section: Real Data Analysismentioning

confidence: 99%

Classical and Bayesian Inference for the Inverse Lomax Distribution Under Adaptive Progressive Type-II Censored Data with COVID-19 Application

Hora

Kabdwal

Srivastava

2022

JRSS

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In this paper, we consider the classical and the Bayesian inferences for unknown parameters of inverse Lomax distribution and their corresponding survival characteristics under the adaptive progressive type-II censoring scheme. In the classical setup, first we obtain the maximum likelihood estimates for the unknown shape parameter of the distribution and its corresponding survival characteristics. Further, we consider symmetric and asymmetric loss functions for the estimation of shape parameter and its corresponding survival characteristics under the Bayesian paradigm. The performances of various derived estimators were recorded using Markov chain Monte Carlo simulation technique for different sample sizes. Finally, a COVID-19 mortality data set is provided to illustrate the computation of various estimators.

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“…e schematic representation of the adaptive Type-II progressive censoring scheme is shown in Figure 1. Cramer and Iliopoulos [2], Mahmoud et al [3], Balakrishnan and Cramer [4], Ye et al [5], El-Sayed et al [6], Mohie El-Din et al [7], Almetwally et al [8], Almetwally et al [9], Nassr et al [10], Mohan and Chacko [11], Almongy et al [12], Haj Ahmad et al [13], Dutta and Kayal [14,15], and Abo-Kasem et al [16] provide comprehensive reviews of the literature on the adaptive Type-II progressive censoring scheme.…”

Section: Introductionmentioning

confidence: 99%

Statistical Inference for the Gompertz Distribution Based on Adaptive Type-II Progressive Censoring Scheme

Amein

El-Saady

Shrahili

et al. 2022

Mathematical Problems in Engineering

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The topic of estimating the parameters of Gompertz distribution using an adaptive Type-II progressively censored data are described in this paper. The unknown parameters, the reliability, and the hazard functions are estimated using maximum likelihood and Bayesian estimation methods. The approximate confidence intervals of them are then determined. Furthermore, the Markov chain Monte Carlo approach is used to perform a Bayesian estimate procedure and compute the credible intervals. Finally, a Monte Carlo simulation study is done to assess the performance of the two estimating methods, and a numerical example with real data is shown to demonstrate the procedures’ utility.

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Estimation of parameters of Kumaraswamy-exponential distribution based on adaptive type-II progressive censored schemes

Cited by 12 publications

References 12 publications

Statistical Inference and Optimal Design of Accelerated Life Testing for the Chen Distribution under Progressive Type-II Censoring

Statistical Inference and Optimal Design of Accelerated Life Testing for the Chen Distribution under Progressive Type-II Censoring

Classical and Bayesian Inference for the Inverse Lomax Distribution Under Adaptive Progressive Type-II Censored Data with COVID-19 Application

Statistical Inference for the Gompertz Distribution Based on Adaptive Type-II Progressive Censoring Scheme

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