Statistical inference for oscillation processes
A new model for time series with a specific oscillation pattern is proposed. The model consists of a hidden phase process controlling the speed of polling and a nonparametric curve characterizing the pattern, leading together to a generalized state space model. Identifiability of the model is proved...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
2017
|
| In: |
Statistics
Year: 2016, Volume: 51, Issue: 1, Pages: 61-83 |
| ISSN: | 1029-4910 |
| DOI: | 10.1080/02331888.2016.1266985 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1080/02331888.2016.1266985
|
| Author Notes: | Rainer Dahlhaus, Thierry Dumont, Sylvain Le Corff & Jan C. Neddermeyer |
| Summary: | A new model for time series with a specific oscillation pattern is proposed. The model consists of a hidden phase process controlling the speed of polling and a nonparametric curve characterizing the pattern, leading together to a generalized state space model. Identifiability of the model is proved and a method for statistical inference based on a particle smoother and a nonparametric EM algorithm is developed. In particular, the oscillation pattern and the unobserved phase process are estimated. The proposed algorithms are computationally efficient and their performance is assessed through simulations and an application to human electrocardiogram recordings. |
|---|---|
| Item Description: | Published online: 27 Dec 2016 Gesehen am 08.05.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1029-4910 |
| DOI: | 10.1080/02331888.2016.1266985 |