Yonekura, S;
Beskos, A;
Singh, SS;
(2021)
Asymptotic analysis of model selection criteria for general hidden Markov models.
Stochastic Processes and their Applications
, 132
pp. 164-191.
10.1016/j.spa.2020.10.006.
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Abstract
The paper obtains analytical results for the asymptotic properties of Model Selection Criteria – widely used in practice – for a general family of hidden Markov models (HMMs), thereby substantially extending the related theory beyond typical ‘i.i.d.-like’ model structures and filling in an important gap in the relevant literature. In particular, we look at the Bayesian and Akaike Information Criteria (BIC and AIC) and the model evidence. In the setting of nested classes of models, we prove that BIC and the evidence are strongly consistent for HMMs (under regularity conditions), whereas AIC is not weakly consistent. Numerical experiments support our theoretical results.
| Type: | Article |
|---|---|
| Title: | Asymptotic analysis of model selection criteria for general hidden Markov models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.spa.2020.10.006 |
| Publisher version: | https://doi.org/10.1016/j.spa.2020.10.006 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10112847 |
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