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Title: Bayesian outlier detection in non‐Gaussian autoregressive time series
Author: Silva, Maria Eduarda
Pereira, Isabel
McCabe, Brendan
Keywords: Convolution closed infinitely divisible models
Additive outliers
Bayesian framework
Time series of counts
State space models
Issue Date: Sep-2019
Publisher: Wiley
Abstract: This work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.
Peer review: yes
DOI: 10.1111/jtsa.12439
ISSN: 0143-9782
Appears in Collections:CIDMA - Artigos
DMat - Artigos
PSG - Artigos

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