The Bayesian Design of Kaplan Meier Estimation Using Gibbs Sampling: Application in econometrics of duration models
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Abstract
A Bayesian approach to survival offers practical, simple and relatively easy
solutions to exploit digitally. In this contribution, we will demonstrate the effectiveness of
the Bayesian approach in the modeling of durations and in an econometric context, we
propose the Bayesian design of the Kaplan Meier estimator based on the stochastic
approximation, which is represented here by the Gibbs sampling. Our contribution is to
improve the deductive stage in estimating nonparametric survival times and under
censorship, and this is what we reached in our research by means of the hierarchical prior
distribution.
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