EM Algorithm for Truncated and Censored Poisson Likelihoods
© 2016 The Authors. The aim of this study is to find the maximum likelihood estimate (MLE) among frequency count data by using the expectation-maximization (EM) algorithm in which is useful to impute the missing or hidden values. Two forms of missing count data in both zero truncation and right cens...
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th-mahidol.435222019-03-14T15:04:35Z EM Algorithm for Truncated and Censored Poisson Likelihoods Chukiat Viwatwongkasem Mahidol University Computer Science © 2016 The Authors. The aim of this study is to find the maximum likelihood estimate (MLE) among frequency count data by using the expectation-maximization (EM) algorithm in which is useful to impute the missing or hidden values. Two forms of missing count data in both zero truncation and right censoring situations are illustrated for estimating the population size on drug use. The results show that a truncated and censored Poisson likelihood performs well with good estimates corresponding to the EM algorithm with a numerically stable convergence, a monotone increasing likelihood, and providing local maxima, so the expected global maximum of the MLE depends on the initial value. 2018-12-11T02:41:09Z 2019-03-14T08:04:35Z 2018-12-11T02:41:09Z 2019-03-14T08:04:35Z 2016-01-01 Conference Paper Procedia Computer Science. Vol.86, (2016), 240-243 10.1016/j.procs.2016.05.109 18770509 2-s2.0-84999751727 https://repository.li.mahidol.ac.th/handle/123456789/43522 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84999751727&origin=inward |
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Computer Science Chukiat Viwatwongkasem EM Algorithm for Truncated and Censored Poisson Likelihoods |
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© 2016 The Authors. The aim of this study is to find the maximum likelihood estimate (MLE) among frequency count data by using the expectation-maximization (EM) algorithm in which is useful to impute the missing or hidden values. Two forms of missing count data in both zero truncation and right censoring situations are illustrated for estimating the population size on drug use. The results show that a truncated and censored Poisson likelihood performs well with good estimates corresponding to the EM algorithm with a numerically stable convergence, a monotone increasing likelihood, and providing local maxima, so the expected global maximum of the MLE depends on the initial value. |
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Mahidol University |
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Mahidol University Chukiat Viwatwongkasem |
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Conference or Workshop Item |
| author |
Chukiat Viwatwongkasem |
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Chukiat Viwatwongkasem |
| title |
EM Algorithm for Truncated and Censored Poisson Likelihoods |
| title_short |
EM Algorithm for Truncated and Censored Poisson Likelihoods |
| title_full |
EM Algorithm for Truncated and Censored Poisson Likelihoods |
| title_fullStr |
EM Algorithm for Truncated and Censored Poisson Likelihoods |
| title_full_unstemmed |
EM Algorithm for Truncated and Censored Poisson Likelihoods |
| title_sort |
em algorithm for truncated and censored poisson likelihoods |
| publishDate |
2018 |
| url |
https://repository.li.mahidol.ac.th/handle/123456789/43522 |
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1763488721474158592 |