Expectation-Maximization Algorithms for Obtaining Estimations of Generalized Failure Intensity Parameters
| Autor: | Makram Krit, Khaled Mili |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
021103 operations research
General Computer Science Computer science Iterative method Maximum likelihood Monte Carlo method Interval estimation 0211 other engineering and technologies 02 engineering and technology Missing data 01 natural sciences Confidence interval 010104 statistics & probability Expectation–maximization algorithm 0101 mathematics Algorithm Reliability (statistics) Parametric statistics |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 7 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2016.070158 |
| Popis: | This paper presents several iterative methods based on Stochastic Expectation-Maximization (EM) methodology in order to estimate parametric reliability models for randomly lifetime data. The methodology is related to Maximum Likelihood Estimates (MLE) in the case of missing data. A bathtub form of failure intensity formulation of a repairable system reliability is presented where the estimation of its parameters is considered through EM algorithm . Field of failures data from industrial site are used to fit the model. Finally, the interval estimation basing on large-sample in literature is discussed and the examination of the actual coverage probabilities of these confidence intervals is presented using Monte Carlo simulation method. |
| Databáze: | OpenAIRE |
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