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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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    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.
    3. Di Crescenzo, Antonio & Longobardi, Maria, 2004. "A measure of discrimination between past lifetime distributions," Statistics & Probability Letters, Elsevier, vol. 67(2), pages 173-182, April.
    4. Mark Briers & Arnaud Doucet & Simon Maskell, 2010. "Smoothing algorithms for state–space models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 61-89, February.
    5. Paul Fearnhead & David Wyncoll & Jonathan Tawn, 2010. "A sequential smoothing algorithm with linear computational cost," Biometrika, Biometrika Trust, vol. 97(2), pages 447-464.
    6. K. Burke & G. MacKenzie, 2017. "Multi-parameter regression survival modeling: An alternative to proportional hazards," Biometrics, The International Biometric Society, vol. 73(2), pages 678-686, June.
    7. Dani Gamerman, 1991. "Dynamic Bayesian Models for Survival Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 63-79, March.
    8. Sourish Das & Dipak K. Dey, 2013. "On Dynamic Generalized Linear Models with Applications," Methodology and Computing in Applied Probability, Springer, vol. 15(2), pages 407-421, June.
    9. K. Hemming & J. E. H. Shaw, 2002. "A parametric dynamic survival model applied to breast cancer survival times," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 421-435, October.
    10. Fahrmeir, Ludwig & Kneib, Thomas, 2011. "Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data," OUP Catalogue, Oxford University Press, number 9780199533022.
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