A Dynamic Changepoint Model for Detecting the Onset of Growth in Bacteriological Infections

Joe Whittaker and Sylvia Frühwirth‐Schnatter

Journal of the Royal Statistical Society Series C, 1994, vol. 43, issue 4, 625-640

Abstract: We consider a structural component model based on a random walk that incorporates a drift from an unknown point in time, τ, with the objective of providing an on‐line estimate of this changepoint. The application to detecting bacteriological growth in routine monitoring of feedstuff motivates the analysis, and the ability of this model to be tuned in different ways for different specific applications is the reason for its choice. The changepoint τ is regarded as a parameter and the posterior distribution (or likelihood function) of τ is computed at each time point by running a triangular multiprocess Kalman filter. The values of other parameters in the structural component model are tuned from previous data. The location and width of an 80% posterior interval give both an estimate of the changepoint and the magnitude of the evidence for a change. A more formal decision rule for on‐line and post‐sampling detection is derived by application of Bayesian decision analysis.

Date: 1994
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2307/2986261

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:43:y:1994:i:4:p:625-640

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().