Time Series of Counts under Censoring: A Bayesian Approach

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Isabel Silva (Autor) FEUP; Maria Eduarda Silva (Autor) FEP; Pereira, I (Autor) Outra; McCabe, B (Autor) Outra

Revista

Título: Entropy

Vol. 25 Nº 549

Página Final: 549

ISSN: 1099-4300

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15