Ml Estimation in the Poisson Binomial-Distribution with Grouped Data Via the Em Algorithm
| dc.contributor.author | Adamidis, K. | en |
| dc.contributor.author | Loukas, S. | en |
| dc.date.accessioned | 2015-11-24T17:25:26Z | |
| dc.date.available | 2015-11-24T17:25:26Z | |
| dc.identifier.issn | 0094-9655 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13042 | |
| dc.rights | Default Licence | - |
| dc.subject | em algorithm | en |
| dc.subject | grouped data | en |
| dc.subject | neyman type a distribution | en |
| dc.subject | poisson binomial distribution | en |
| dc.title | Ml Estimation in the Poisson Binomial-Distribution with Grouped Data Via the Em Algorithm | en |
| heal.abstract | The maximum likelihood estimation of parameters of the Poisson binomial distribution, based on a sample with exact and grouped observations, is considered by applying the EM algorithm (Dempster et al., 1977). The results of Louis (1982) are used in obtaining the observed information matrix and accelerating the convergence of the EM algorithm substantially. The maximum likelihood estimation from samples consisting entirely of complete (Sprott, 1958) or grouped observations are treated as special cases of the estimation problem mentioned above. A brief account is given for the implementation of the EM algorithm when the sampling distribution is the Neyman Type A since the latter is a limiting form of the Poisson binomial. Numerical examples based on real data are included. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.secondary | <Go to ISI>://A1993QM82000003 | - |
| heal.journalName | Journal of Statistical Computation and Simulation | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 1993 | - |
| heal.publisher | Taylor & Francis | en |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μαθηματικών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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