On Nonparametric Estimation of a Reliability Function

Zardasht, Vali; Zeephongsekul, Panlop; Asadi, Majid

doi:10.1080/03610926.2010.535629

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…Since the reliability means a probability, it is necessary to evaluate the tolerance limits of the estimated probability value. Reliability practitioners have proved the tolerance and confidence limits by the mathematical methods [5]. For a two-sided limit on reliability, the interval is…”

Section: Field-reliability Lifetime Estimation Processmentioning

confidence: 99%

Lifetime Prediction for A Certain Missile Based on Field Data

Zhang¹,

Sun²,

Liu³

2018

Proceedings of the 2018 8th International Conference on Applied Science, Engineering and Technology (ICASET 2018)

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A statistical procedure of the lifetime prediction for a certain missile is established by the reliability analysis using the field data. First, the reliability at given time point is estimated by the Kaplan-Meier method according to samples undergoing long time storage. Second, the lifetime distribution mode is obtained by fitting the relationship of the reliability and time with linear regression method. Third, parameters of the lifetime distribution are calculated by the Newton-Raphson method. Finally, the lifetime of a certain missile is predicted with the right censored field data. Results show that the field-reliability method could provide a clear reference model to estimate the lifetime of a certain missile through the field data and give some useful feedbacks for future design improvements.

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Section: Field-reliability Lifetime Estimation Processmentioning

confidence: 99%

Lifetime Prediction for A Certain Missile Based on Field Data

Zhang¹,

Sun²,

Liu³

2018

Proceedings of the 2018 8th International Conference on Applied Science, Engineering and Technology (ICASET 2018)

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“…The characteristics of the developed non-parametric estimation of the reliability function that are used in many reliability problems are studied in Zardasht et al [18]. Neamah et al [19] developed a fuzzy reliability estimate through the Frechet probability using a simulation method.…”

Section: Introductionmentioning

confidence: 99%

Statistical inference for stress-strength reliability using inverse Lomax lifetime distribution with mechanical engineering applications

et al. 2022

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The inverse Lomax distribution has been extensively used in many disciplines, including stochastic modelling, economics, actuarial sciences, and life testing. It is among the most recognizable lifetime models. The purpose of this research is to look into a new and important aspect of the inverse Lomax distribution: the calculation of the fuzzy stress-strength reliability parameter RF = P(Y < X), as?suming X and Y are random independent variables that follow the inverse Lomax probability distribution. The properties of structural for the proposed reliability model are studied along with the Bayesian estimation methods, maximum product of the spacing and maximum likelihood. Extensive simulation studies are achieved to explore the performance of the various estimates. Subsequently, two sets of real data are considered to highlight the practicability of the model.

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“…[2] discussed the Marshall-Model: Olkin's comparison of interval estimations for P(X < Y), Ref. [3] introduced nonparametric reliability function estimation, [4] presented an overview of fuzzy sets, fuzzy logic, and fuzzy control systems. Furthermore, based on an exponential distribution, a fuzzy Bayesian system's reliability is assessed in [5][6][7], presenting fuzzy probability and statistics, Ref.…”

Section: Introductionmentioning

confidence: 99%

“…[10] showed the most effective techniques for testing fuzzy hypotheses with conflicting data, Ref. [3] studied nonparametric estimation of a reliability function, Ref. [11] presented the use of median and ranked set sampling approaches, and stress-strength reliability for exponentiated Pareto distribution can be estimated.…”

Section: Introductionmentioning

confidence: 99%

Inference of Reliability Analysis for Type II Half Logistic Weibull Distribution with Application of Bladder Cancer

Mohamed

Tolba

Almetwally

et al. 2022

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The estimation of the unknown parameters of Type II Half Logistic Weibull (TIIHLW) distribution was analyzed in this paper. The maximum likelihood and Bayes methods are used as estimation methods. These estimators are used to estimate the fuzzy reliability function and to choose the best estimator of the fuzzy reliability function by comparing the mean square error (MSE). The simulation’s results showed that fuzziness is better than reality for all sample sizes, and fuzzy reliability at Bayes predicted estimates is better than the maximum likelihood technique. It produces the lowest average MSE until a sample size of n = 50 is obtained. A simulated data set is applied to diagnose the performance of the two techniques applied here. A real data set is used as a practice for the model discussed and developed the maximum likelihood estimate alternative model of TIIHLW as Topp Leone inverted Kumaraswamy, modified Kies inverted Topp–Leone, Kumaraswamy Weibull–Weibull, Marshall–Olkin alpha power inverse Weibull, and odd Weibull inverted Topp–Leone. We conclude that the TIIHLW is the best distribution fit for this data.

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On Nonparametric Estimation of a Reliability Function

Cited by 8 publications

References 20 publications

Lifetime Prediction for A Certain Missile Based on Field Data

Lifetime Prediction for A Certain Missile Based on Field Data

Statistical inference for stress-strength reliability using inverse Lomax lifetime distribution with mechanical engineering applications

Inference of Reliability Analysis for Type II Half Logistic Weibull Distribution with Application of Bladder Cancer

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