Book Chapter10.1007/978-3-030-70665-4_182
Parameter Optimization Estimation Based on Mixed Exponential Weibull Distribution
Xiaoqin Zhang,Yu Wang,Dianjun Lu +2 more
- 01 Aug 2020
- pp 1679-1686
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TL;DR: In this article, a mixed exponential Weibull distribution parameter estimation model is established under the condition of small samples and the convergence is analyzed theoretically, the validity and reliability of the algorithm are verified by data simulation.
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Abstract: Mixed exponential Weibull distribution is an important statistical model in life data analysis. It is difficult to estimate the parameters of this model due to its large number of parameters using traditional moment estimation and maximum likelihood estimation. Using EM algorithm and ECM algorithm to estimate model parameters and convergence is better in large sample. In this paper, a mixed exponential Weibull distribution parameter estimation model is established under the condition of small samples. The probability graph estimation method and L-M algorithm are used to solve the optimization problem. The convergence is analyzed theoretically. Finally, the validity and reliability of the algorithm are verified by data simulation.
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Citations
Parameter Estimation of the Weibull Distribution in Modeling the Reliability of Technical Objects
Mykhaylo Frolov,Serhiy Tanchenko,Liubov Ohluzdina +2 more
TL;DR: The Weibull distribution is widely used for reliability modeling of technical objects. Parameter estimation of the Weibull distribution often ignores the probabilistic character of the parameters. This paper suggests a simplified approach for estimating Weibull distribution parameters that is efficient for engineering practice.
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Parameter estimation for mixed-Weibull distribution
Dimitri Kececioglu,Wendai Wang +1 more
- 19 Jan 1998
TL;DR: In this paper, a new approach is developed to estimate the mixed-Weibull distribution's parameters, where the population sample data are split into subpopulation data sets over the whole test duration by using the posterior belonging probability of each observation to each subpopulation.
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