期刊
ANNALS OF APPLIED STATISTICS
卷 5, 期 3, 页码 2150-2168出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/11-AOAS471
关键词
Bayesian statistics; model selection; Bayes factors; Joinpoint regression; epidemiological time series
资金
- Ministerio de Educacion y Ciencia [MTM2007-61554, MTM2010-19528]
- Fondo de Investigaciones Sanitarias. Instituto de Salud Carlos III [ISCIII06-PI1742]
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 Castellon, a province in Spain.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据