Maximum Likelihood Estimation for an Observation Driven Model for Poisson Counts
This paper is concerned with an observation-driven model for time series of counts whose conditional distribution given past observations follows a Poisson distribution. This class of models is capable of modeling a wide range of dependence structures and is readily estimated using an approximation...
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| Veröffentlicht in: | Methodology and computing in applied probability 2005-06, Vol.7 (2), p.149-159 |
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| container_title | Methodology and computing in applied probability |
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| creator | Davis, Richard A. Dunsmuir, William T. M. Streett, Sarah B. |
| description | This paper is concerned with an observation-driven model for time series of counts whose conditional distribution given past observations follows a Poisson distribution. This class of models is capable of modeling a wide range of dependence structures and is readily estimated using an approximation to the likelihood function. Recursive formulae for carrying out maximum likelihood estimation are provided and the technical components required for establishing a central limit theorem of the maximum likelihood estimates are given in a special case. [PUBLICATION ABSTRACT] |
| doi_str_mv | 10.1007/s11009-005-1480-4 |
| format | Article |
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| ispartof | Methodology and computing in applied probability, 2005-06, Vol.7 (2), p.149-159 |
| issn | 1387-5841 1573-7713 |
| language | eng |
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| source | EBSCOhost Business Source Complete; Springer Nature - Complete Springer Journals |
| subjects | Approximation Counting Mathematical analysis Mathematical models Maximum likelihood estimates Maximum likelihood estimation Recursive Studies Time series |
| title | Maximum Likelihood Estimation for an Observation Driven Model for Poisson Counts |
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