Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series

• Type: Article in International Scientific Journal
• Date: 2019

Authors

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

Journal

Title: Journal of Time Series Analysis

Vol. 40

Pages: 631-648

ISSN: 0143-9782

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00P-ZDP

DOI: 10.1111/jtsa.12439

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18