A quantile variant of the expectation–maximization algorithm and its application to parameter estimation with interval data
The expectation–maximization algorithm is a powerful computational technique for finding the maximum likelihood estimates for parametric models when the data are not fully observed. The expectation–maximization is best suited for situations where the expectation in each E-step and the maximization i...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2018-09-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1177/1748301818779007 |