Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Quantifying the uncertainty in change points

Tools
- Tools
+ Tools

Nam, Christopher F. H., Aston, John A. D. and Johansen, Adam M. (2011) Quantifying the uncertainty in change points. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Volume 2011 (Number 19).

[img]
Preview
Text
WRAP_Johansen_11-19w.pdf - Published Version

Download (467Kb)
Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...

Request Changes to record.

Abstract

Quantifying the uncertainty in the location and nature of change points in time series is important
in a variety of applications. Many existing methods for estimation of the number and location of
change points fail to capture fully or explicitly the uncertainty regarding these estimates, whilst
others require explicit simulation of large vectors of dependent latent variables.
This paper proposes methodology for approximating the full posterior distribution of various
change point characteristics in the presence of parameter uncertainty. The methodology combines
recent work on evaluation of exact change point distributions conditional on model parameters via
Finite Markov Chain Imbedding in a Hidden Markov Model setting, and accounting for parameter
uncertainty and estimation via Bayesian modelling and Sequential Monte Carlo. The combination of
the two leads to a
exible and computationally efficient procedure, which does not require estimates
of the underlying state sequence.
We illustrate that good estimation of posterior distributions regarding change point characteristics
is provided for simulated and functional magnetic resonance imaging data. We use the methodology
to show that the modelling of relevant physical properties of the scanner can in
uence detection of
change points and their uncertainty.

Item Type: Working or Discussion Paper (Working Paper)
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Change-point problems
Series Name: Working papers
Publisher: University of Warwick. Centre for Research in Statistical Methodology
Place of Publication: Coventry
Official Date: 31 May 2011
Dates:
DateEvent
31 May 2011Published
Volume: Volume 2011
Number: Number 19
Institution: University of Warwick
Status: Not Peer Reviewed
Access rights to Published version: Open Access
Funder: Engineering and Physical Sciences Research Council (EPSRC), Higher Education Funding Council for England (HEFCE)
Grant number: EP/H016856/1 (EPSRC), EP/I017984/1 (EPSRC)
Version or Related Resource: Nam, Christopher F. H., Aston, John A. D. and Johansen, Adam M. (2012). Quantifying the uncertainty in change points. Journal of Time Series Analysis, Volume 33 (Number 5). pp. 807-823. ISSN 0143-9782 http://wrap.warwick.ac.uk/id/eprint/44378
Related URLs:
  • http://dx.doi.org/10.1111/j.1467-9892.20...

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us