pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution

pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution

: MCMC, Bayesian inference, semiparametric, survival analysis

In this study, we present a new module built for users interested in a programming
language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE)
distribution. The module is an extension to the open-source program JAGS by which a
Gibbs sampler can be applied without requiring the derivation of complete conditionals
and the subsequent implementation of strategies to draw samples from unknown distributions. The PE distribution is widely used in the fields of survival analysis and reliability.
Currently, it can only be implemented in JAGS through methods to indirectly specify the
likelihood based on the Poisson or Bernoulli probabilities. Our module provides a more
straightforward implementation and is thus more attractive to the researchers aiming to
spend more time exploring the results from the Bayesian inference rather than implementing the Markov Chain Monte Carlo algorithm. For those interested in extending JAGS,
this work can be seen as a tutorial including important information not well investigated
or organized in other materials. Here, we describe how to use the module taking advantage
of the interface between R and JAGS. A short simulation study is developed to ensure
that the module behaves well and a real illustration, involving two PE models, exhibits a
context where the module can be used in practice.

Vinícius D. Mayrink

https://www.jstatsoft.org/article/view/v100i08

Universidade Federal
de Minas Gerais

November 2021

Fajar bagus W

PDF

English

Text