Tuned iterated filtering
(2013) In Statistics and Probability Letters 83(9). p.2077-2080- Abstract
- Iterated filtering is an algorithm for estimating parameters in partially observed Markov process (POMP) models. The real-world performance of the algorithm depends on several tuning parameters. We propose a simple method for optimizing the parameter governing the joint dynamics of the hidden parameter process (called the Sigma matrix). The tuning is implemented using a fixed-lag sequential Monte Carlo expectation maximization (EM) algorithm. We introduce two different versions of the tuning parameter, the approximately estimated Sigma matrix, and a normalized version of the same matrix. Our simulations show that the finite-sample performance for the normalized matrix outperform the standard iterated filter, while the naive version is... (More)
- Iterated filtering is an algorithm for estimating parameters in partially observed Markov process (POMP) models. The real-world performance of the algorithm depends on several tuning parameters. We propose a simple method for optimizing the parameter governing the joint dynamics of the hidden parameter process (called the Sigma matrix). The tuning is implemented using a fixed-lag sequential Monte Carlo expectation maximization (EM) algorithm. We introduce two different versions of the tuning parameter, the approximately estimated Sigma matrix, and a normalized version of the same matrix. Our simulations show that the finite-sample performance for the normalized matrix outperform the standard iterated filter, while the naive version is doing more harm than good. (C) 2013 Elsevier B.V. All rights reserved. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4042666
- author
- Lindström, Erik LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Hidden Markov models, Sequential Monte Carlo methods, Maximum likelihood, estimation
- in
- Statistics and Probability Letters
- volume
- 83
- issue
- 9
- pages
- 2077 - 2080
- publisher
- Elsevier
- external identifiers
-
- wos:000322295000022
- scopus:84879164818
- ISSN
- 0167-7152
- DOI
- 10.1016/j.spl.2013.05.019
- language
- English
- LU publication?
- yes
- id
- ead07296-b2d1-4235-866b-339201f58eb3 (old id 4042666)
- date added to LUP
- 2016-04-01 14:51:28
- date last changed
- 2022-01-28 02:53:23
@article{ead07296-b2d1-4235-866b-339201f58eb3, abstract = {{Iterated filtering is an algorithm for estimating parameters in partially observed Markov process (POMP) models. The real-world performance of the algorithm depends on several tuning parameters. We propose a simple method for optimizing the parameter governing the joint dynamics of the hidden parameter process (called the Sigma matrix). The tuning is implemented using a fixed-lag sequential Monte Carlo expectation maximization (EM) algorithm. We introduce two different versions of the tuning parameter, the approximately estimated Sigma matrix, and a normalized version of the same matrix. Our simulations show that the finite-sample performance for the normalized matrix outperform the standard iterated filter, while the naive version is doing more harm than good. (C) 2013 Elsevier B.V. All rights reserved.}}, author = {{Lindström, Erik}}, issn = {{0167-7152}}, keywords = {{Hidden Markov models; Sequential Monte Carlo methods; Maximum likelihood; estimation}}, language = {{eng}}, number = {{9}}, pages = {{2077--2080}}, publisher = {{Elsevier}}, series = {{Statistics and Probability Letters}}, title = {{Tuned iterated filtering}}, url = {{http://dx.doi.org/10.1016/j.spl.2013.05.019}}, doi = {{10.1016/j.spl.2013.05.019}}, volume = {{83}}, year = {{2013}}, }