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New approximate Bayesian computation algorithm for censored data

Author

Listed:
  • Kristin McCullough

    (Grand View University)

  • Tatiana Dmitrieva

    (Advocate Aurora Health)

  • Nader Ebrahimi

    (Northern Illinois University)

Abstract

Approximate Bayesian computation refers to a family of algorithms that perform Bayesian inference under intractable likelihoods. In this paper we propose replacing the distance metric in certain algorithms with hypothesis testing. The benefits of which are that summary statistics are no longer required and censoring can be present in the observed data set without needing to simulate any censored data. We illustrate our proposed method through a nanotechnology application in which we estimate the concentration of particles in a liquid suspension. We prove that our method results in an approximation to the true posterior and that the parameter estimates are consistent. We further show, through comparative analysis, that it is more efficient than existing methods for censored data.

Suggested Citation

  • Kristin McCullough & Tatiana Dmitrieva & Nader Ebrahimi, 2022. "New approximate Bayesian computation algorithm for censored data," Computational Statistics, Springer, vol. 37(3), pages 1369-1397, July.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:3:d:10.1007_s00180-021-01167-3
    DOI: 10.1007/s00180-021-01167-3
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    References listed on IDEAS

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