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What About the Posterior Distributions When the Model is Non-dominated? Read chapter Read first chapter

2015 | OriginalPaper | Chapter

29. The Exponential-Poisson Regression Model for Recurrent Events: A Bayesian Approach

Authors : Márcia A. C. Macera, Francisco Louzada, Vicente G. Cancho

Published in: Interdisciplinary Bayesian Statistics

Publisher: Springer International Publishing

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Abstract

In this chapter, we introduce a new regression model for recurrent event data, in which the time of each recurrence is associated to one or multiple latent causes and no information is provided about the cause responsible for the event occurrence. This model is characterized by a fully parametric rate function and it is based on the exponential-Poisson distribution. The time of each recurrence is then given by the minimum lifetime value among all latent causes. Inference aspects of the proposed model are discussed via Bayesian inference by using Markov Chain Monte Carlo (MCMC) method. A simulation study investigates the frequentist properties of the posterior estimators for different sample sizes. A real-data application demonstrates the use of the proposed model.

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previous chapter Assessing a Spatial Boost Model for Quantitative Trait GWAS
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Footnotes
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Metadata
Title
The Exponential-Poisson Regression Model for Recurrent Events: A Bayesian Approach
Authors
Márcia A. C. Macera
Francisco Louzada
Vicente G. Cancho
Copyright Year
2015
Book
Interdisciplinary Bayesian Statistics

Print ISBN: 978-3-319-12453-7

Electronic ISBN: 978-3-319-12454-4

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-12454-4_29

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