1
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Nadeb H, Torabi H, Zhao Y. The generalized order statistics arising from three populations with the lower truncated proportional hazard rate models and its application to the sensitivity to the early disease stage. J Biopharm Stat 2024:1-24. [PMID: 38907670 DOI: 10.1080/10543406.2024.2365978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 06/05/2024] [Indexed: 06/24/2024]
Abstract
In this paper, we present some results to make inference about the parameters of lower truncated proportional hazard rate models with the same baseline distributions based on three independent generalized order statistics samples. Then, especially by considering the results of the diagnostic tests for the non-diseased, early-diseased stage and fully diseased populations, we make inference about sensitivity to the early disease stage parameter. The maximum likelihood estimator, a generalized pivotal estimator and some Bayes estimators are obtained for different structures of prior distributions. The percentile bootstrap confidence interval, a generalized pivotal confidence interval and some Bayesian credible intervals are also presented. A Monte Carlo simulation study is used to evaluate the performances of the obtained point estimators and confidence/credible intervals and two competitors. We use two real datasets to illustrate the proposed methods.
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Affiliation(s)
- Hossein Nadeb
- Department of Statistics, Yazd University, Yazd, Iran
| | - Hamzeh Torabi
- Department of Statistics, Yazd University, Yazd, Iran
| | - Yichuan Zhao
- Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA
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2
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Alharbi R, Garg R, Kumar I, Kumari A, Aldallal R. On estimation of P(Y < X) for inverse Pareto distribution based on progressively first failure censored data. PLoS One 2023; 18:e0287473. [PMID: 38032903 PMCID: PMC10688691 DOI: 10.1371/journal.pone.0287473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 07/04/2023] [Indexed: 12/02/2023] Open
Abstract
The stress-strength reliability (SSR) model ϕ = P(Y < X) is used in numerous disciplines like reliability engineering, quality control, medical studies, and many more to assess the strength and stresses of the systems. Here, we assume X and Y both are independent random variables of progressively first failure censored (PFFC) data following inverse Pareto distribution (IPD) as stress and strength, respectively. This article deals with the estimation of SSR from both classical and Bayesian paradigms. In the case of a classical point of view, the SSR is computed using two estimation methods: maximum product spacing (MPS) and maximum likelihood (ML) estimators. Also, derived interval estimates of SSR based on ML estimate. The Bayes estimate of SSR is computed using the Markov chain Monte Carlo (MCMC) approximation procedure with a squared error loss function (SELF) based on gamma informative priors for the Bayesian paradigm. To demonstrate the relevance of the different estimates and the censoring schemes, an extensive simulation study and two pairs of real-data applications are discussed.
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Affiliation(s)
- Randa Alharbi
- Department of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudia Arabia
| | - Renu Garg
- Department of Statistics, Kirori Mal College, University of Delhi, Delhi, India
| | - Indrajeet Kumar
- Department of Mathematics, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India
| | - Anita Kumari
- Department of Statistics, Central University of Haryana, Mahendergarh, India
| | - Ramy Aldallal
- Department of Accounting, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Jeddah, Saudi Arabia
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3
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Jia J, Yan Z, Song H, Chen Y. Reliability estimation in multicomponent stress–strength model for generalized inverted exponential distribution. Soft comput 2022. [DOI: 10.1007/s00500-022-07628-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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4
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Kumari A, Kumar S, Kumar K. Inference for reliability in a multicomponent stress-strength model from generalized inverted exponential lifetime distribution under progressive first failure censoring. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2122970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Anita Kumari
- Department of Statistics, Central University of Haryana, Mahendergarh India
| | - Shrawan Kumar
- Department of Statistics, Kirori Mal College, Delhi, India
| | - Kapil Kumar
- Department of Statistics, Central University of Haryana, Mahendergarh India
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5
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Bi Q, Ma Y, Gui W. Reliability estimation for the bathtub-shaped distribution based on progressively first-failure censoring sampling. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2020.1746338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Qixuan Bi
- Department of Mathematics, Beijing Jiaotong University, Beijing, China
| | - Yanbin Ma
- Department of Mathematics, Beijing Jiaotong University, Beijing, China
| | - Wenhao Gui
- Department of Mathematics, Beijing Jiaotong University, Beijing, China
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6
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Optimal Plan of Multi-Stress–Strength Reliability Bayesian and Non-Bayesian Methods for the Alpha Power Exponential Model Using Progressive First Failure. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071306] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
It is extremely frequent for systems to fail in their demanding operating environments in many real-world contexts. When systems reach their lowest, highest, or both extreme operating conditions, they usually fail to perform their intended functions, which is something that researchers pay little attention to. The goal of this paper is to develop inference for multi-reliability using unit alpha power exponential distributions for stress–strength variables based on the progressive first failure. As a result, the problem of estimating the stress–strength function R, where X, Y, and Z come from three separate alpha power exponential distributions, is addressed in this paper. The conventional methods, such as maximum likelihood for point estimation, Bayesian and asymptotic confidence, boot-p, and boot-t methods for interval estimation, are also examined. Various confidence intervals have been obtained. Monte Carlo simulations and real-world application examples are used to evaluate and compare the performance of the various proposed estimators.
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7
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Bayesian and Non-Bayesian Inference for Weibull Inverted Exponential Model under Progressive First-Failure Censoring Data. MATHEMATICS 2022. [DOI: 10.3390/math10101648] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this article, the estimation of the parameters and the reliability and hazard functions for Weibull inverted exponential (WIE) distribution is considered based on progressive first-failure censoring (PFFC) data. For non-Bayesian inference, maximum likelihood (ML) estimators are acquired; meanwhile, their existence is verified. Via asymptotic normality of ML estimators and delta method, the corresponding confidence intervals (CIs) of the parameters and the reliability and hazard functions are constructed. For Bayesian inference, Lindley’s approximation and Markov chain Monte Carlo (MCMC) techniques are proposed to obain the Bayes estimators and the corresponding credible intervals (CRIs). To this end, both symmetric and asymmetric loss functions are used. A large number of Monte Carlo simulations are implemented to evaluate the efficiency of the developed methods. Eventually, a numerical example is analyzed for illustrative purposes.
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8
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Bayesian Inferential Approaches and Bootstrap for the Reliability and Hazard Rate Functions under Progressive First-Failure Censoring for Coronavirus Data from Asymmetric Model. Symmetry (Basel) 2022. [DOI: 10.3390/sym14050956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
This paper deals with the estimation of the parameters for asymmetric distribution and some lifetime indices such as reliability and hazard rate functions based on progressive first-failure censoring. Maximum likelihood, bootstrap and Bayesian approaches of the distribution parameters and reliability characteristics are investigated. Furthermore, the approximate confidence intervals and highest posterior density credible intervals of the parameters are constructed based on the asymptotic distribution of the maximum likelihood estimators and Markov chain Monte Carlo technique, respectively. In addition, the delta method is implemented to obtain the variances of the reliability and hazard functions. Moreover, we apply two methods of bootstrap to construct the confidence intervals. The Bayes inference based on the squared error and LINEX loss functions is obtained. Extensive simulation studies are conducted to evaluate the behavior of the proposed methods. Finally, a real data set of the COVID-19 mortality rate is analyzed to illustrate the estimation methods developed here.
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9
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Reliability inference for multicomponent stress–strength model from Kumaraswamy-G family of distributions based on progressively first failure censored samples. Comput Stat 2022. [DOI: 10.1007/s00180-021-01180-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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10
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Multi Stress-Strength Reliability Based on Progressive First Failure for Kumaraswamy Model: Bayesian and Non-Bayesian Estimation. Symmetry (Basel) 2021. [DOI: 10.3390/sym13112120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
It is highly common in many real-life settings for systems to fail to perform in their harsh operating environments. When systems reach their lower, upper, or both extreme operating conditions, they frequently fail to perform their intended duties, which receives little attention from researchers. The purpose of this article is to derive inference for multi reliability where stress-strength variables follow unit Kumaraswamy distributions based on the progressive first failure. Therefore, this article deals with the problem of estimating the stress-strength function, R when X,Y, and Z come from three independent Kumaraswamy distributions. The classical methods namely maximum likelihood for point estimation and asymptotic, boot-p and boot-t methods are also discussed for interval estimation and Bayes methods are proposed based on progressive first-failure censored data. Lindly’s approximation form and MCMC technique are used to compute the Bayes estimate of R under symmetric and asymmetric loss functions. We derive standard Bayes estimators of reliability for multi stress–strength Kumaraswamy distribution based on progressive first-failure censored samples by using balanced and unbalanced loss functions. Different confidence intervals are obtained. The performance of the different proposed estimators is evaluated and compared by Monte Carlo simulations and application examples of real data.
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11
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Saini S, Tomer S, Garg R. On the reliability estimation of multicomponent stress–strength model for Burr XII distribution using progressively first-failure censored samples. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2021.1970165] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Shubham Saini
- Department of Statistics, University of Delhi, Delhi, India
| | - Sachin Tomer
- Department of Statistics, Ramanujan College, University of Delhi, Delhi, India
| | - Renu Garg
- Department of Statistics, University of Delhi, Delhi, India
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12
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Inference for Inverse Power Lomax Distribution with Progressive First-Failure Censoring. ENTROPY 2021; 23:e23091099. [PMID: 34573724 PMCID: PMC8466453 DOI: 10.3390/e23091099] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 11/28/2022]
Abstract
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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13
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Decision-Making for the Lifetime Performance Index. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:3005067. [PMID: 34335712 PMCID: PMC8292028 DOI: 10.1155/2021/3005067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 07/02/2021] [Indexed: 11/18/2022]
Abstract
The purpose of this research is to develop a maximum likelihood estimator (MLE) for lifetime performance index CL for the parameter of mixture Rayleigh-Half Normal distribution (RHN) under progressively type-II right-censored samples under the constraint of knowing the lower specification limit (L). Additionally, we suggest an asymptotic normal distribution for the MLE for CL in order to construct a mechanism for evaluating products' lifespan efficiency. We have specified all the steps to carry out the test. Additionally, not only does hypothesis testing successfully assess the lifetime performance of items, but it also functions as a supplier selection criterion for the consumer. Finally, we have added two real data examples as illustration examples. These two applications are provided to demonstrate how the results can be applied.
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14
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Rady EHA, Hassanein W, Yehia S. Evaluation of the lifetime performance index on first failure progressive censored data based on Topp Leone Alpha power exponential model applied on HPLC data. J Biopharm Stat 2021; 31:565-582. [PMID: 34029156 DOI: 10.1080/10543406.2021.1895192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
In this paper, the statistical inference of the lifetime performance index for the first failure progressive censoring schemes for the Topp Leone Alpha power exponential distribution (TLAPE) which excluded from a new structure of distribution models called T-Alpha Power X (T-APX) family is introduced. The proposed statistical inference of the lifetime performance index is applied on the High-Performance Liquid Chromatography data of blood samples from organ transplant recipients which is known as HPLC. The goodness of fit criteria of TLAPE for HPLC data proved the potentiality of TLAPE compared with other well-known distributions. This result has an effect on achieving the required results in testing procedure of the Lifetime performance index. Moreover, the statistical and reliability characteristics of TLAPE are studied. A simulation study is performed to examine the performance of the ML parameter estimates in terms of bias and mean square error. Two real data sets for survival and breast cancer are modeled using the TLAPE distribution and compared with other well-known distributions, to illustrate its performance. The results emphasize that the TLAPE distribution has a superior fitting performance to cancer data than the compared distributions.
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Affiliation(s)
- El-Houssainy A Rady
- Applied Statistics and Econometric Department, I.S.S.R Cairo University, Giza, Egypt
| | - Wafaa Hassanein
- Mathematics Department, Faculty of Science, Tanta University, Tanta, Egypt
| | - Shaimaa Yehia
- Mathematics Department, Faculty of Science, Tanta University, Tanta, Egypt
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15
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Classical and Bayesian Inference for a Progressive First-Failure Censored Left-Truncated Normal Distribution. Symmetry (Basel) 2021. [DOI: 10.3390/sym13030490] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Point and interval estimations are taken into account for a progressive first-failure censored left-truncated normal distribution in this paper. First, we derive the estimators for parameters on account of the maximum likelihood principle. Subsequently, we construct the asymptotic confidence intervals based on these estimates and the log-transformed estimates using the asymptotic normality of maximum likelihood estimators. Meanwhile, bootstrap methods are also proposed for the construction of confidence intervals. As for Bayesian estimation, we implement the Lindley approximation method to determine the Bayesian estimates under not only symmetric loss function but also asymmetric loss functions. The importance sampling procedure is applied at the same time, and the highest posterior density (HPD) credible intervals are established in this procedure. The efficiencies of classical statistical and Bayesian inference methods are evaluated through numerous simulations. We conclude that the Bayes estimates given by Lindley approximation under Linex loss function are highly recommended and HPD interval possesses the narrowest interval length among the proposed intervals. Ultimately, we introduce an authentic dataset describing the tensile strength of 50mm carbon fibers as an illustrative sample.
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16
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Saini S, Chaturvedi A, Garg R. Estimation of stress–strength reliability for generalized Maxwell failure distribution under progressive first failure censoring. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2020.1856846] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Shubham Saini
- Department of Statistics, University of Delhi, Delhi, India
| | | | - Renu Garg
- Department of Statistics, University of Delhi, Delhi, India
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17
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Amiri L, Ganjali M, Hashemi R, Khazaei M. The competing risks analysis for parallel and series systems using Type-II progressive censoring. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2019.1620779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Leila Amiri
- Department of Statistics, Shahid Beheshti University, Tehran, Iran
| | - Mojtaba Ganjali
- Department of Statistics, Shahid Beheshti University, Tehran, Iran
| | - Reza Hashemi
- Department of Statistics, Razi University, Kermanshah, Iran
| | - Mojtaba Khazaei
- Department of Statistics, Shahid Beheshti University, Tehran, Iran
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18
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Inferences and Optimal Censoring Schemes for Progressively First-Failure Censored Nadarajah-Haghighi Distribution. SANKHYA A 2020. [DOI: 10.1007/s13171-019-00175-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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19
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Laumen B, Cramer E. Stage life testing with random stage changing times. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2020.1805764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Benjamin Laumen
- Department of Mathematics, Institute of Statistics, RWTH Aachen University, Aachen, Germany
| | - Erhard Cramer
- Department of Mathematics, Institute of Statistics, RWTH Aachen University, Aachen, Germany
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20
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Statistical Inference of the Lifetime Performance Index with the Log-Logistic Distribution Based on Progressive First-Failure-Censored Data. Symmetry (Basel) 2020. [DOI: 10.3390/sym12060937] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Estimating the accurate evaluation of product lifetime performance has always been a hot topic in manufacturing industry. This paper, based on the lifetime performance index, focuses on its evaluation when a lower specification limit is given. The progressive first-failure-censored data we discuss have a common log-logistic distribution. Both Bayesian and non-Bayesian method are studied. Bayes estimator of the parameters of the log-logistic distribution and the lifetime performance index are obtained using both the Lindley approximation and Monte Carlo Markov Chain methods under symmetric and asymmetric loss functions. As for interval estimation, we apply the maximum likelihood estimator to construct the asymptotic confidence intervals and the Metropolis–Hastings algorithm to establish the highest posterior density credible intervals. Moreover, we analyze a real data set for demonstrative purposes. In addition, different criteria for deciding the optimal censoring scheme have been studied.
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21
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Parameter and Reliability Inferences of Inverted Exponentiated Half-Logistic Distribution under the Progressive First-Failure Censoring. MATHEMATICS 2020. [DOI: 10.3390/math8050708] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Using progressive first-failure censored samples, we mainly study the inferences of the unknown parameters and the reliability and failure functions of the Inverted Exponentiated Half-Logistic distribution. The progressive first-failure censoring is an extension and improvement of progressive censoring, which is of great significance in the field of lifetime research. Besides maximum likelihood estimation, we use Bayesian estimation under unbalanced and balanced losses: General Entropy loss function, Squared Error loss function and Linex loss function. Approximate explicit expression of Bayesian estimation is given using Lindley approximation method for point estimation and Metropolis-Hastings method for point and interval estimation. Bayesian credible intervals and asymptotic confidence intervals are derived in the form of average length and coverage probability. To show the research effects, a simulation study and practical data analysis are carried out. Finally, we discuss the optimal censoring mode under four different criteria.
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22
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Abd El‐Monsef MME, Hassanein WAAE. Assessing the lifetime performance index for Kumaraswamy distribution under first‐failure progressive censoring scheme for ball bearing revolutions. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2020; 36:1086-1097. [DOI: 10.1002/qre.2616] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 12/27/2019] [Indexed: 09/01/2023]
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23
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Yu J, Gui W, Shan Y. Statistical Inference on the Shannon Entropy of Inverse Weibull Distribution under the Progressive First-Failure Censoring. ENTROPY 2019. [PMCID: PMC7514555 DOI: 10.3390/e21121209] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Entropy is an uncertainty measure of random variables which mathematically represents the prospective quantity of the information. In this paper, we mainly focus on the estimation for the parameters and entropy of an Inverse Weibull distribution under progressive first-failure censoring using classical (Maximum Likelihood) and Bayesian methods. For Bayesian approaches, the Bayesian estimates are obtained based on both asymmetric (General Entropy, Linex) and symmetric (Squared Error) loss functions. Due to the complex form of Bayes estimates, we cannot get an explicit solution. Therefore, the Lindley method as well as Importance Sampling procedure is applied. Furthermore, using Importance Sampling method, the Highest Posterior Density credible intervals of entropy are constructed. As a comparison, the asymptotic intervals of entropy are also gained. Finally, a simulation study is implemented and a real data set analysis is performed to apply the previous methods.
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24
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Inference for the Chen Distribution Under Progressive First-Failure Censoring. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2019. [DOI: 10.1007/s42519-019-0052-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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25
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Wang BX, Yu Q. Interval estimation for the generalized inverted exponential distribution under progressive first failure censoring. J STAT COMPUT SIM 2019. [DOI: 10.1080/00949655.2019.1577429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Bing Xing Wang
- Department of Statistics, Zhejiang Gongshang University, Hangzhou, People's Republic of China
| | - Qian Yu
- Department of Statistics, Zhejiang Gongshang University, Hangzhou, People's Republic of China
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26
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Estimation and Prediction for a Progressively First-Failure Censored Inverted Exponentiated Rayleigh Distribution. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2019. [DOI: 10.1007/s42519-019-0038-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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27
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28
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Panahi H. Inference for exponentiated Pareto distribution based on progressive first-failure censored data with application to cumin essential oil data. JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2018. [DOI: 10.1080/09720510.2018.1479209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Hanieh Panahi
- Department of Mathematics and Statistics, Islamic Azad University, Lahijan Branch, P.O. Box 1616, Lahijan, Iran
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29
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Ahmadi MV, Doostparast M. Pareto analysis for the lifetime performance index of products on the basis of progressively first-failure-censored batches under balanced symmetric and asymmetric loss functions. J Appl Stat 2018. [DOI: 10.1080/02664763.2018.1541170] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
| | - Mahdi Doostparast
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
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30
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Hassanein WA. Statistical inference of the lifetime performance index for Lindley distribution under progressive first-failure censoring scheme applied to HPLC data. INT J BIOMATH 2018. [DOI: 10.1142/s1793524518500730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper is devoted to the construct of the maximum likelihood estimator of the lifetime performance index based on first-failure progressive right type II censored sample for Lindley distribution. Statistical inference for assessing the lifetime performance of the items is performed. Finally, two examples are given, one of them considers a real life application of blood samples from organ transplant recipient using the liquid chromatography (HPLC) data and the other is a simulated example to illustrate the proposed statistical procedure.
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31
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Ahmadi MV, Doostparast M, Ahmadi J. Block censoring scheme with two-parameter exponential distribution. J STAT COMPUT SIM 2018. [DOI: 10.1080/00949655.2018.1426763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Mohammad Vali Ahmadi
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
- Present address: Department of Statistics, University of Bojnord, PO Box 94531-1339 Bojnord, Iran
| | - Mahdi Doostparast
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Jafar Ahmadi
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
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32
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Krishna H, Dube M, Garg R. Estimation of P(Y < X) for progressively first-failure-censored generalized inverted exponential distribution. J STAT COMPUT SIM 2017. [DOI: 10.1080/00949655.2017.1326119] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Hare Krishna
- Department of Statistics, Ch. Charan Singh University, Meerut, India
| | - Madhulika Dube
- Department of Statistics, Maharshi Dayanand University, Rohtak, India
| | - Renu Garg
- Department of Statistics, Maharshi Dayanand University, Rohtak, India
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Hermanns M, Cramer E. Likelihood inference for the component lifetime distribution based on progressively censored parallel systems data. J STAT COMPUT SIM 2016. [DOI: 10.1080/00949655.2016.1222392] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Ahmed EA. Estimation and prediction for the generalized inverted exponential distribution based on progressively first-failure-censored data with application. J Appl Stat 2016. [DOI: 10.1080/02664763.2016.1214692] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Essam A. Ahmed
- Faculty of Business Administration, Taibah University, Khyber, Saudi Arabia
- Mathematics Department, Sohag University, Sohag, Egypt
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Mohammed HS, Ateya SF, AL-Hussaini EK. Estimation based on progressive first-failure censoring from exponentiated exponential distribution. J Appl Stat 2016. [DOI: 10.1080/02664763.2016.1214245] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Heba S. Mohammed
- Mathematics Department, Faculty of Science, Assiut University, New Valley Branch, Assiut, Egypt
- Mathematical Science, Faculty of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Saieed F. Ateya
- Mathematics Department, Faculty of Science, Assiut University, Assiut, Egypt
- Mathematics and Statistics Department, Faculty of Science, Taif University, Taif, Saudi Arabia
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Dube M, Krishna H, Garg R. Generalized inverted exponential distribution under progressive first-failure censoring. J STAT COMPUT SIM 2015. [DOI: 10.1080/00949655.2015.1052440] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Singh S, Tripathi YM. Reliability sampling plans for a lognormal distribution under progressive first-failure censoring with cost constraint. Stat Pap (Berl) 2014. [DOI: 10.1007/s00362-014-0608-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Soliman AA, Abd Ellah AH, Abou-Elheggag NA, Modhesh AA. Estimation from Burr type XII distribution using progressive first-failure censored data. J STAT COMPUT SIM 2013. [DOI: 10.1080/00949655.2012.690157] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Soliman AA, Ellah AHA, Abou-Elheggag NA, El-Sagheer RM. Estimation Based on Progressive First-Failure Censored Sampling with Binomial Removals. INTELLIGENT INFORMATION MANAGEMENT 2013; 05:117-125. [DOI: 10.4236/iim.2013.54012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Soliman AA, Abd Ellah AH, Abou-Elheggag NA, Modhesh AA. Estimation of the coefficient of variation for non-normal model using progressive first-failure-censoring data. J Appl Stat 2012. [DOI: 10.1080/02664763.2012.725466] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Soliman AA, Abd-Ellah AH, Abou-Elheggag NA, Abd-Elmougod GA. Estimation of the parameters of life for Gompertz distribution using progressive first-failure censored data. Comput Stat Data Anal 2012. [DOI: 10.1016/j.csda.2012.01.025] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Progressively first-failure censored reliability sampling plans with cost constraint. Comput Stat Data Anal 2012. [DOI: 10.1016/j.csda.2011.12.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Soliman AA, Abou-elheggag NA, Abd ellah AH, Modhesh AA. Bayesian and non-Bayesian inferences of the Burr-XII distribution for progressive first-failure censored data. METRON 2012; 70:1-25. [DOI: 10.1007/bf03263568] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Soliman AA, Abd Ellah AH, Abou-Elheggag NA, Abd-Elmougod GA. A simulation-based approach to the study of coefficient of variation of Gompertz distribution under progressive first-failure censoring. INDIAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2011; 42:335-356. [DOI: 10.1007/s13226-011-0022-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Soliman AA, Ellah AHA, Abou-Elheggag NA, Modhesh AA. Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling. INTELLIGENT INFORMATION MANAGEMENT 2011; 03:175-185. [DOI: 10.4236/iim.2011.35021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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