scholarly journals Bayesian Inference for the Kumaraswamy Distribution under Generalized Progressive Hybrid Censoring

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1032
Author(s):  
Jiayi Tu ◽  
Wenhao Gui
Keyword(s):  
Real Life ◽  
Unknown Parameters ◽  
Simulation Research ◽  
Kumaraswamy Distribution ◽  
Hybrid Censoring ◽  
Bayesian Estimates ◽  
Squared Error ◽  
Linex Loss ◽  
Lindley Approximation ◽  
Censoring Scheme

Incomplete data are unavoidable for survival analysis as well as life testing, so more and more researchers are beginning to study censoring data. This paper discusses and considers the estimation of unknown parameters featured by the Kumaraswamy distribution on the condition of generalized progressive hybrid censoring scheme. Estimation of reliability is also considered in this paper. To begin with, the maximum likelihood estimators are derived. In addition, Bayesian estimators under not only symmetric but also asymmetric loss functions, like general entropy, squared error as well as linex loss function, are also offered. Since the Bayesian estimates fail to be of explicit computation, Lindley approximation, as well as the Tierney and Kadane method, is employed to obtain the Bayesian estimates. A simulation research is conducted for the comparison of the effectiveness of the proposed estimators. A real-life example is employed for illustration.

scholarly journals Estimating the Entropy for Lomax Distribution Based on Generalized Progressively Hybrid Censoring

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1219 ◽  
Author(s):  
Shuhan Liu ◽  
Wenhao Gui
Keyword(s):  
Incomplete Data ◽  
Real Data ◽  
Unknown Parameters ◽  
Data Set ◽  
Hybrid Censoring ◽  
Lomax Distribution ◽  
Bayesian Estimates ◽  
Squared Error ◽  
General Entropy Loss Function ◽  
Two Parameter

As it is often unavoidable to obtain incomplete data in life testing and survival analysis, research on censoring data is becoming increasingly popular. In this paper, the problem of estimating the entropy of a two-parameter Lomax distribution based on generalized progressively hybrid censoring is considered. The maximum likelihood estimators of the unknown parameters are derived to estimate the entropy. Further, Bayesian estimates are computed under symmetric and asymmetric loss functions, including squared error, linex, and general entropy loss function. As we cannot obtain analytical Bayesian estimates directly, the Lindley method and the Tierney and Kadane method are applied. A simulation study is conducted and a real data set is analyzed for illustrative purposes.

scholarly journals Estimation procedures on Type-II censored data from a scaled Muth distribution

2021 ◽  
pp. 148-158
Author(s):  
Hayrinisa Demirci BIÇER
Keyword(s):  
Censored Data ◽  
Mean Squared Error ◽  
Real Life ◽  
Model Parameters ◽  
Type Ii ◽  
Monte Carlo Simulation Study ◽  
Anal Ysis ◽  
Squared Error ◽  
Type Ii Censored Data ◽  
Censoring Scheme

In the present paper, we consider the estimation problem for the scaled Muth distribution under Type-II censoring scheme. In order to estimate the model parameters α and β, the maximum likelihood, the least-squares, and the maximum spacing estimators are derived. To show estimation efficiencies of the estimators obtained with this paper, we present an exten- sive Monte-Carlo simulation study in which the estimators are compared according to bias and mean squared error criteria. Furthermore, we evaluate the applicability of the scaled Muth distribution by taking into account both full and Type-II censored data situations by an anal- ysis conducted on a real-life dataset.

scholarly journals Estimation for Flexible Weibull Extension-Burr XII Distribution under Adaptive Type-II Progressive Censoring Scheme

2021 ◽  
pp. 1065-1094
Author(s):  
Rania M. Kamal ◽  
Moshira A. Ismail
Keyword(s):  
Real Life ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Type Ii ◽  
Progressive Censoring ◽  
Unknown Parameters ◽  
Data Set ◽  
Burr Xii Distribution ◽  
Credible Intervals ◽  
Censoring Scheme

In this paper, based on an adaptive Type-II progressive censoring scheme, estimation of flexible Weibull extension-Burr XII distribution is discussed. Maximum likelihood estimation and asymptotic confidence intervals of the unknown parameters are obtained. The adaptive Metropolis (AM) method is applied to carry out a Bayesian estimation procedure under symmetric and asymmetric loss functions and calculate the credible intervals. A simulation study is carried out to assess the performance of the estimators. Finally, a real life data set is used for illustration purpose.

Optimum reliability acceptance sampling plan using Type-I generalized hybrid censoring scheme for products under warranty

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jimut Bahan Chakrabarty ◽  
Shovan Chowdhury ◽  
Soumya Roy
Keyword(s):  
Optimal Design ◽  
Cost Function ◽  
Real Life ◽  
Optimal Solution ◽  
Type I ◽  
Content Type ◽  
Hybrid Censoring ◽  
Acceptance Criterion ◽  
Acceptance Sampling Plan ◽  
Censoring Scheme

PurposeThe purpose of this paper is to design an optimal reliability acceptance sampling plan (RASP) using the Type-I generalized hybrid censoring scheme (GHCS) for non-repairable products sold under the general rebate warranty. A cost function approach is proposed for products having Weibull distributed lifetimes incorporating relevant costs.Design/methodology/approachFor Weibull distributed product lifetimes, acceptance criterion introduced by Lieberman and Resnikoff (1955) is derived for Type-I GHCS. A cost function is formulated using expected warranty cost and other relevant cost components incorporating the acceptance criterion. The cost function is optimized following a constrained optimization approach to arrive at the optimum RASP. The constraint ensures that the producer's and the consumer's risks are maintained at agreed-upon levels.FindingsOptimal solution using the above approach is obtained for Type-I GHCS. As a special case of Type-I GHCS, the proposed approach is also used to arrive at the optimal design for Type-I hybrid censoring scheme as shown in Chakrabarty et al. (2019). Observations regarding the change in optimal design and computational times between the two censoring schemes are noted. An extensive simulation study is performed to validate the model for finite sample sizes and the results obtained are found to be in strong agreement. In order to analyze the sensitivity of the optimal solution due to misspecification of parameter values and cost components, a well-designed sensitivity analysis is carried out using a real-life failure data set from Lawless (2003). Interesting observations are made regarding the change in optimal cost due to change in parameter values, the impact of warranty cost in optimal design and change in optimal design due to change in lot sizes.Originality/valueThe research presents an approach for designing optimal RASPs using Type-I generalized hybrid censoring. The study formulates optimum life test sampling plans by minimizing the average aggregate costs involved, which makes it valuable in dealing with real-life problems pertaining to product quality management.

Reliability Analysis for the Burr XII Units under Random Censoring

2013 ◽  
Vol 321-324 ◽  
pp. 2265-2268
Author(s):  
Xiao Lin Shi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Loss Functions ◽  
Random Censoring ◽  
Unknown Parameters ◽  
Bayesian Estimates ◽  
Censored Samples ◽  
Linex Loss ◽  
Two Parameter ◽  
Burr Type Xii

Based on random censored samples, the problem of estimating unknown parameters and reliability performances of the two-parameter Burr type XII units is considered. Firstly, we obtained the Bayesian estimates of the parameter for the two-parameter Burr type XII distribution under the asymmetric Linex loss functions. Secondly, the Bayesian estimates of the reliability performances are derived. In order to investigate the accuracy of estimations, an illustrative example is examined numerically by the Monte-Carlo simulation.

scholarly journals A New Two-Parameter Burr-Hatke Distribution: Properties and Bayesian and Non-Bayesian Inference with Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Ahmed Z. Afify ◽  
Hassan M. Aljohani ◽  
Abdulaziz S. Alghamdi ◽  
Ahmed M. Gemeay ◽  
Abdullah M. Sarg
Keyword(s):  
Real Life ◽  
Estimation Methods ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Bayesian Techniques ◽  
Bathtub Shape ◽  
Mathematical Properties ◽  
Squared Error ◽  
And Behavior ◽  
Two Parameter

This article introduces a two-parameter flexible extension of the Burr-Hatke distribution using the inverse-power transformation. The failure rate of the new distribution can be an increasing shape, a decreasing shape, or an upside-down bathtub shape. Some of its mathematical properties are calculated. Ten estimation methods, including classical and Bayesian techniques, are discussed to estimate the model parameters. The Bayes estimators for the unknown parameters, based on the squared error, general entropy, and linear exponential loss functions, are provided. The ranking and behavior of these methods are assessed by simulation results with their partial and overall ranks. Finally, the flexibility of the proposed distribution is illustrated empirically using two real-life datasets. The analyzed data shows that the introduced distribution provides a superior fit than some important competing distributions such as the Weibull, Fréchet, gamma, exponential, inverse log-logistic, inverse weighted Lindley, inverse Pareto, inverse Nakagami-M, and Burr-Hatke distributions.

scholarly journals Bayesian and E-Bayesian estimation for the Kumaraswamy distribution based on type-ii censoring

2016 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Hesham Reyad ◽  
Soha Othman Ahmed
Keyword(s):  
Bayesian Estimation ◽  
Loss Function ◽  
Quadratic Loss ◽  
Type Ii ◽  
Linex Loss Function ◽  
Kumaraswamy Distribution ◽  
Squared Error ◽  
Linex Loss ◽  
Symmetric Loss ◽  
Bayesian Estimators

<p>This paper introduces the Bayesian and E-Bayesian estimation for the shape parameter of the Kumaraswamy distribution based on type-II censored schemes. These estimators are derived under symmetric loss function [squared error loss (SELF))] and three asymmetric loss functions [LINEX loss function (LLF), Degroot loss function (DLF) and Quadratic loss function (QLF)]. Monte Carlo simulation is performed to compare the E-Bayesian estimators with the associated Bayesian estimators in terms of Mean Square Error (MSE).</p>

scholarly journals Bayesian and E-Bayesian Estimations of Bathtub-Shaped Distribution under Generalized Type-I Hybrid Censoring

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 934
Author(s):  
Yuxuan Zhang ◽  
Kaiwei Liu ◽  
Wenhao Gui
Keyword(s):  
Bayesian Method ◽  
Real Data ◽  
Loss Functions ◽  
Type I ◽  
Statistical Efficiency ◽  
Unknown Parameters ◽  
Data Set ◽  
Hybrid Censoring ◽  
Bayesian Estimations ◽  
Generalized Type

For the purpose of improving the statistical efficiency of estimators in life-testing experiments, generalized Type-I hybrid censoring has lately been implemented by guaranteeing that experiments only terminate after a certain number of failures appear. With the wide applications of bathtub-shaped distribution in engineering areas and the recently introduced generalized Type-I hybrid censoring scheme, considering that there is no work coalescing this certain type of censoring model with a bathtub-shaped distribution, we consider the parameter inference under generalized Type-I hybrid censoring. First, estimations of the unknown scale parameter and the reliability function are obtained under the Bayesian method based on LINEX and squared error loss functions with a conjugate gamma prior. The comparison of estimations under the E-Bayesian method for different prior distributions and loss functions is analyzed. Additionally, Bayesian and E-Bayesian estimations with two unknown parameters are introduced. Furthermore, to verify the robustness of the estimations above, the Monte Carlo method is introduced for the simulation study. Finally, the application of the discussed inference in practice is illustrated by analyzing a real data set.

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