New approximate Bayesian computation algorithm for censored data

Kristin McCullough (), Tatiana Dmitrieva () and Nader Ebrahimi ()
Additional contact information
Kristin McCullough: Grand View University
Tatiana Dmitrieva: Advocate Aurora Health
Nader Ebrahimi: Northern Illinois University

Computational Statistics, 2022, vol. 37, issue 3, No 15, 1369-1397

Abstract: 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.

Keywords: Approximate Bayesian computation; Censoring; Bias; Consistency; Hypothesis testing; Stochastic equivalence (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01167-3

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