NDLI: An objective Bayesian analysis of the change point problem

Content Provider	Springer Nature Link
Author	More, Elías Casella, George Garcia Ferrer, Antonio
Copyright Year	2005
Abstract	The Bayesian literature on the change point problem deals with the inference of a change in the distribution of a set of time-ordered data based on a sample of fixed size. This is the so-called “retrospective or off-line” analysis of the change point problem. A related but different problem is that of the “sequential” change point detection, mainly analyzed from a frequentist viewpoint. While the former typically focuses on the estimation of the position in which the change point occurs, the latter is a testing problem which has a natural formulation as a Bayesian model selection problem. In this paper we provide such a Bayesian formulation, which generalizes previous formulations such as the well-known CUSUM stopping rule. We show that the conventional improper priors (also called non-informative, objective or default), cannot be used either for sequential detection of the change or for retrospective estimation. Then, we propose objective intrinsic prior distributions for the unknown model parameters. The normal and Poisson cases are studied in detail and examples with simulated and real data are provided.
Starting Page	191
Ending Page	204
Page Count	14
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	19
Issue Number	3
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-03-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Change point Intrinsic priors Model selection Sequential detection Retrospective estimation
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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