Interval estimation of multicomponent stress–strength reliability based on inverse Weibull distribution

Jana, Nabakumar; Bera, Samadrita

doi:10.1016/j.matcom.2021.07.026

Cited by 26 publications

(10 citation statements)

References 28 publications

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“…Liu et al [7] proposed the reliability estimation of a N-M-cold standby redundancy system in a multicomponent stress-strength model with generalized half-logistic distribution. Related work can also be found in the literatures Wang et al [8], Wang et al [9], Saini et al [10] for the single-component stress-strength model and in Kohansal [11], Zhang et al [12], Jana and Bera [13], and Dey et al [14] for the multicomponent stress-strength model, and the references therein.…”

Section: Introductionmentioning

confidence: 94%

Copula-based Reliability Analysis for Multicomponent System Stress-Strength Model under Progressively Censoring

Cai

Yang

et al. 2023

Preprint

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In reliability analysis, the stress-strength model is of importance and interest issue. Usually, the stress variable and the strength variable are assumed to be independent. However, such an assumption may be unrealistic in some applications. In this paper, we consider the statistical analysis of multicomponent system stress-strength model when the stress and strength are dependent variables, and the dependency is modeled by two different copula functions. We obtain the maximum likelihood estimates of the model parameters and the expression for the probability R=P(Y<X). Confidence intervals are constructed based on the asymptotic normality of maximum likelihood estimate. In addition, to test the dependence between the stress and strength variables, we discuss the likelihood ratio tests for hypotheses of interest. Finally, simulation studies and one real data example are provided to support the proposed model and methods, and to examine the performance of estimators and testing.

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Section: Introductionmentioning

confidence: 94%

Copula-based Reliability Analysis for Multicomponent System Stress-Strength Model under Progressively Censoring

Cai

Yang

et al. 2023

Preprint

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“…Prakash [16] investigated the Bayesian prediction on optimum SS-PALT in a generalized inverted exponential distribution under a two-sample approach. Jana and Bera [17] proposed the interval estimation of multicomponent stress-strength reliability for the inverse Weibull distribution. Tripathi [18] discussed whether the types of lower and upper records affect the maximum likelihood estimates of the parameters for inverse Rayleigh and exponential distributions.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Interval Estimation for the Two-Parameter Exponential Distribution Based on the Right Type II Censored Sample

2022

Symmetry

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The Bayesian interval estimation of the scale parameter for two-parameter exponential distribution is proposed based on the right type II censored sample. Under this type of censoring, two methods of Bayesian joint confidence region of the two parameters are also proposed. The simulation results show that the Bayesian method has a higher coverage probability than the existing method, so the Bayesian method is recommended for use. This research is related to the topic of asymmetrical probability distributions and applications across disciplines. The predictive interval of the future observation based on the right type II censored sample is also provided. One biometrical example is given to illustrate the proposed methods for the Bayesian interval estimations and prediction interval.

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“…The three-parameter IW distribution has been constructed by De Gusmo, et al [9]. Its properties have been discussed by Oluyede and Yang [10] and Jana and Bera [11]. Using the Marshall-Olkin method, another three-parameter IW model has been presented by Okasha, et al [12].…”

Section: Introductionmentioning

confidence: 99%

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2022

AJAMS

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This manuscript presents a new univariate six parameters type I half-logistic inverse Weibull distribution. Explicit expressions for the quantile function, the moments, the moment generating function and the maximum likelihood estimators are formulated. Simulation is employed to investigate the goodness of fit and to discuss the behaviour of the new model. Competitive models are compared via real data. The univariate one is used as a base line to construct a bivariate one named bivariate six parameters type I half-logistic inverse Weibull distribution. Mathematical properties of the new bivariate distrib-ution are investigated. The goodness of fit and the model performance are discussed via simulation. COVID-19 mortality data for Italy and Canada are treated as a bivariate random variable to prove the applicability of the new bivariate distribution.

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Interval estimation of multicomponent stress–strength reliability based on inverse Weibull distribution

Cited by 26 publications

References 28 publications

Copula-based Reliability Analysis for Multicomponent System Stress-Strength Model under Progressively Censoring

Copula-based Reliability Analysis for Multicomponent System Stress-Strength Model under Progressively Censoring

Bayesian Interval Estimation for the Two-Parameter Exponential Distribution Based on the Right Type II Censored Sample

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