Likelihood inference for exponential-trawl processes
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2015
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| _version_ | 1866910493634985984 |
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| author | Shephard, Neil Yang, Justin J. |
| author_facet | Shephard, Neil Yang, Justin J. |
| contents | Integer-valued trawl processes are a class of serially correlated, stationary and infinitely divisible processes that Ole E. Barndorff-Nielsen has been working on in recent years. In this Chapter, we provide the first analysis of likelihood inference for trawl processes by focusing on the so-called exponential-trawl process, which is also a continuous time hidden Markov process with countable state space. The core ideas include prediction decomposition, filtering and smoothing, complete-data analysis and EM algorithm. These can be easily scaled up to adapt to more general trawl processes but with increasing computation efforts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1501_00925 |
| institution | arXiv |
| publishDate | 2015 |
| record_format | arxiv |
| spellingShingle | Likelihood inference for exponential-trawl processes Shephard, Neil Yang, Justin J. Methodology Probability 62M20 (Primary), 60G55 (Secondary) Integer-valued trawl processes are a class of serially correlated, stationary and infinitely divisible processes that Ole E. Barndorff-Nielsen has been working on in recent years. In this Chapter, we provide the first analysis of likelihood inference for trawl processes by focusing on the so-called exponential-trawl process, which is also a continuous time hidden Markov process with countable state space. The core ideas include prediction decomposition, filtering and smoothing, complete-data analysis and EM algorithm. These can be easily scaled up to adapt to more general trawl processes but with increasing computation efforts. |
| title | Likelihood inference for exponential-trawl processes |
| topic | Methodology Probability 62M20 (Primary), 60G55 (Secondary) |
| url | https://arxiv.org/abs/1501.00925 |