The Exponential-Poisson Regression Model for Recurrent Events: A Bayesian Approach
DOI10.1007/978-3-319-12454-4_29zbMath1364.62224OpenAlexW371800310MaRDI QIDQ5266596
Márcia A. C. Macera, Vicente G. Cancho, Francisco Louzada
Publication date: 16 June 2017
Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-12454-4_29
Bayesian approachexponential-Poisson distributionexponential-Poisson regression model for recurrent eventsMarkov Chain Monte Carlo (MCMC) methodparametric rate function
Applications of statistics to biology and medical sciences; meta analysis (62P10) Non-Markovian processes: estimation (62M09) Bayesian inference (62F15) Reliability and life testing (62N05)
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- Modeling gap times between recurrent events by marginal rate function
- Cox's regression model for counting processes: A large sample study
- Semiparametric model for recurrent event data with excess zeros and informative censoring
- Survival and event history analysis. A process point of view
- A new lifetime distribution
- Inference from iterative simulation using multiple sequences
- Semiparametric inference for a general class of models for recurrent events
- The statistical analysis of recurrent events.
- A model checking method for the proportional hazards model with recurrent gap time data
- Estimation and interpretation of incidence rate difference for recurrent events when the estimation model is misspecified
- On the regression analysis of multivariate failure time data
- Some Simple Robust Methods for the Analysis of Recurrent Events
- Simulation and the Monte Carlo Method
- Statistical models based on counting processes
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