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Efficient particle smoothing for Bayesian inference in dynamic survival models

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  • Parfait Munezero

    (Ericsson)

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.

Suggested Citation

  • Parfait Munezero, 2022. "Efficient particle smoothing for Bayesian inference in dynamic survival models," Computational Statistics, Springer, vol. 37(2), pages 975-994, April.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:2:d:10.1007_s00180-021-01155-7
    DOI: 10.1007/s00180-021-01155-7
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    References listed on IDEAS

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