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September 2011 A Bayesian Joinpoint regression model with an unknown number of break-points
Miguel A. Martinez-Beneito, Gonzalo García-Donato, Diego Salmerón
Ann. Appl. Stat. 5(3): 2150-2168 (September 2011). DOI: 10.1214/11-AOAS471


Joinpoint regression is used to determine the number of segments needed to adequately explain the relationship between two variables. This methodology can be widely applied to real problems, but we focus on epidemiological data, the main goal being to uncover changes in the mortality time trend of a specific disease under study. Traditionally, Joinpoint regression problems have paid little or no attention to the quantification of uncertainty in the estimation of the number of change-points. In this context, we found a satisfactory way to handle the problem in the Bayesian methodology. Nevertheless, this novel approach involves significant difficulties (both theoretical and practical) since it implicitly entails a model selection (or testing) problem. In this study we face these challenges through (i) a novel reparameterization of the model, (ii) a conscientious definition of the prior distributions used and (iii) an encompassing approach which allows the use of MCMC simulation-based techniques to derive the results. The resulting methodology is flexible enough to make it possible to consider mortality counts (for epidemiological applications) as Poisson variables. The methodology is applied to the study of annual breast cancer mortality during the period 1980–2007 in Castellón, a province in Spain.


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Miguel A. Martinez-Beneito. Gonzalo García-Donato. Diego Salmerón. "A Bayesian Joinpoint regression model with an unknown number of break-points." Ann. Appl. Stat. 5 (3) 2150 - 2168, September 2011.


Published: September 2011
First available in Project Euclid: 13 October 2011

zbMATH: 1228.62032
MathSciNet: MR2884935
Digital Object Identifier: 10.1214/11-AOAS471

Keywords: Bayes factors , Bayesian statistics , epidemiological time series , Joinpoint regression , Model selection

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.5 • No. 3 • September 2011
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