Time Series of Counts under Censoring: A Bayesian Approach

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Isabel Silva (Author) FEUP; Maria Eduarda Silva (Author) FEP; Pereira, I (Author) Other; McCabe, B (Author) Other

Journal

Title: Entropy

Vol. 25 No. 549

Final page: 549

ISSN: 1099-4300

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-5FN

DOI: 10.3390/e25040549

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15