Inference for a class of partially observed point process models
This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential Monte Carlo methods, investigating the problems of perfor...
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| Veröffentlicht in: | Annals of the Institute of Statistical Mathematics 2013-06, Vol.65 (3), p.413-437 |
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| container_end_page | 437 |
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| container_title | Annals of the Institute of Statistical Mathematics |
| container_volume | 65 |
| creator | Martin, James S. Jasra, Ajay McCoy, Emma |
| description | This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential Monte Carlo methods, investigating the problems of performing sequential filtering and smoothing in complex examples, where current methods often fail. We consider various approaches for approximating posterior distributions using SMC. Our approaches, with some theoretical discussion are illustrated on a doubly stochastic point process applied in the context of finance. |
| doi_str_mv | 10.1007/s10463-012-0375-8 |
| format | Article |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Approximation Computer simulation Construction Economics Estimating techniques Filtering Filtration Finance Inference Insurance Management Mathematical analysis Mathematical models Mathematics Mathematics and Statistics Monte Carlo simulation Statistics Statistics for Business Studies |
| title | Inference for a class of partially observed point process models |
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