Read our Editor's Choice Article "Time Series of Counts under Censoring: A Bayesian Approach"
Entropy MDPI
Entropy is an international and interdisciplinary peer-reviewed open access journal of entropy and information studies.
Authors: Isabel Silva, Maria Eduarda Silva , Isabel Pereira and Brendan McCabe
Abstract: 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.
Read full article at: https://www.mdpi.com/1099-4300/25/4/549
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