Entropy (Mar 2023)

Time Series of Counts under Censoring: A Bayesian Approach

  • Isabel Silva,
  • Maria Eduarda Silva,
  • Isabel Pereira,
  • Brendan McCabe

DOI
https://doi.org/10.3390/e25040549
Journal volume & issue
Vol. 25, no. 4
p. 549

Abstract

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Censored data are frequently found in diverse fields including environmental monitoring, medicine, economics and social sciences. Censoring occurs when observations are available only for a restricted range, e.g., due to a detection limit. Ignoring censoring produces biased estimates and unreliable statistical inference. The aim of this work is to contribute to the modelling of time series of counts under censoring using convolution closed infinitely divisible (CCID) models. The emphasis is on estimation and inference problems, using Bayesian approaches with Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms.

Keywords