Advanced

Inference for time-varying signals using locally stationary processes

Anderson, Rachele LU and Sandsten, Maria LU (2019) In Journal of Computational and Applied Mathematics 347. p.24-35
Abstract
Locally Stationary Processes (LSPs) in Silverman’s sense, defined by the modulation in time of a stationary covariance function, are valuable in stochastic modelling of time-varying signals. However, for practical applications, methods to conduct reliable parameter inference from measured data are required. In this paper, we address the lack of suitable methods for estimating the parameters of the LSP model, by proposing a novel inference method. The proposed method is based on the separation of the two factors defining the LSP covariance function, in order to take advantage of their individual structure and divide the inference problem into two simpler sub-problems. The method’s performance is tested in a simulation study and compared... (More)
Locally Stationary Processes (LSPs) in Silverman’s sense, defined by the modulation in time of a stationary covariance function, are valuable in stochastic modelling of time-varying signals. However, for practical applications, methods to conduct reliable parameter inference from measured data are required. In this paper, we address the lack of suitable methods for estimating the parameters of the LSP model, by proposing a novel inference method. The proposed method is based on the separation of the two factors defining the LSP covariance function, in order to take advantage of their individual structure and divide the inference problem into two simpler sub-problems. The method’s performance is tested in a simulation study and compared with traditional sample covariance based estimation. An illustrative example of parameter estimation from EEG data, measured during a memory encoding task, is provided. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Computational and Applied Mathematics
volume
347
pages
24 - 35
publisher
Elsevier
external identifiers
  • scopus:85051780493
ISSN
0377-0427
DOI
10.1016/j.cam.2018.07.046
language
English
LU publication?
yes
id
01713532-a814-4e59-8f36-2bbe5b7387df
date added to LUP
2018-08-27 23:01:39
date last changed
2019-01-06 14:02:49
@article{01713532-a814-4e59-8f36-2bbe5b7387df,
  abstract     = {Locally Stationary Processes (LSPs) in Silverman’s sense, defined by the modulation in time of a stationary covariance function, are valuable in stochastic modelling of time-varying signals. However, for practical applications, methods to conduct reliable parameter inference from measured data are required. In this paper, we address the lack of suitable methods for estimating the parameters of the LSP model, by proposing a novel inference method. The proposed method is based on the separation of the two factors defining the LSP covariance function, in order to take advantage of their individual structure and divide the inference problem into two simpler sub-problems. The method’s performance is tested in a simulation study and compared with traditional sample covariance based estimation. An illustrative example of parameter estimation from EEG data, measured during a memory encoding task, is provided.},
  author       = {Anderson, Rachele and Sandsten, Maria},
  issn         = {0377-0427},
  language     = {eng},
  month        = {02},
  pages        = {24--35},
  publisher    = {Elsevier},
  series       = {Journal of Computational and Applied Mathematics},
  title        = {Inference for time-varying signals using locally stationary processes},
  url          = {http://dx.doi.org/10.1016/j.cam.2018.07.046},
  volume       = {347},
  year         = {2019},
}