TY - JOUR
T1 - A dynamic change-point model for detecting the onset of growth of bacteriological infections
AU - Whittaker, Joe
AU - Frühwirth-Schnatter, Sylvia
PY - 1994
Y1 - 1994
N2 - 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.
AB - 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.
UR - http://www.jstor.org/stable/2986261
U2 - 10.2307/2986261
DO - 10.2307/2986261
M3 - Journal article
SN - 0035-9254
VL - 43
SP - 625
EP - 640
JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)
JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)
IS - 4
ER -