Efficient particle smoothing for Bayesian inference in dynamic survival models

Parfait Munezero ()
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Parfait Munezero: Ericsson

Computational Statistics, 2022, vol. 37, issue 2, No 18, 975-994

Abstract: Abstract This article proposes an efficient Bayesian inference for piecewise exponential hazard (PEH) models, which allow the effect of a covariate on the survival time to vary over time. The proposed inference methodology is based on a particle smoothing algorithm that depends on three particle filters. Efficient proposal (importance) distributions for the particle filters tailored to the nature of survival data and PEH models are developed using the Laplace approximation of the posterior distribution and linear Bayes theory. The algorithm is applied to both simulated and real data, and the results show that it is faster and more efficient than a state-of-the-art MCMC sampler, and scales well in high-dimensional and relatively large data.

Keywords: Hazard function; Linear Bayes; Particle filter; Particle smoothing; Piecewise exponential; Survival function (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:37:y:2022:i:2:d:10.1007_s00180-021-01155-7

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DOI: 10.1007/s00180-021-01155-7

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