A model fork-out-of-mload-sharing systems
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Publication:2830183
DOI10.1080/03610926.2014.953691zbMath1349.62510OpenAlexW2342509969MaRDI QIDQ2830183
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Publication date: 9 November 2016
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2014.953691
order statisticsconditional distributiontwo-step estimation procedureproportional reversed hazard rate
Order statistics; empirical distribution functions (62G30) Probability distributions: general theory (60E05) Reliability and life testing (62N05)
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Cites Work
- Reliability estimation based on system data with an unknown load share rule
- On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data
- Survival in dynamic environments
- Dynamic reliability models with conditional proportional hazards
- On the joint distribution of component failures for monotone load-sharing systems
- Sequential order statistics and \(k\)-out-of-\(n\) systems with sequentially adjusted failure rates
- Inference procedures for the linear failure rate model
- Fractional integration of the \(H\)-function of several variables
- A concept of generalized order statistics
- A threshold representation for the strength distribution of a complex load sharing system
- A family of distributions to model load sharing systems
- Dynamic modeling and statistical analysis of event times
- Exponentiated Exponential Family: An Alternative to Gamma and Weibull Distributions
- The exponentiated weibull family: some properties and a flood data application
- Statistics and Time Dependence of Mechanical Breakdown in Fibers
- The Asymptotic Time to Failure of a Mechanical System of Parallel Members
- Exponentiated Weibull family for analyzing bathtub failure-rate data
- Modeling failure time data by lehman alternatives
- Estimating Load-Sharing Properties in a Dynamic Reliability System
- The statistical theory of the strength of bundles of threads. I
- A new look at the statistical model identification
