Bayesian Outlier Detection in Non‐Gaussian Autoregressive Time Series. (2nd December 2018)

• Record Type: Journal Article
• Authors: Silva, Maria Eduarda; Pereira, Isabel; McCabe, Brendan
• Is Part Of: Journal of time series analysis. Volume 40:Number 5(2019)
• Page Start: 631
• Page End: 648
• Publication Date: 2018-12-02
• Subjects: Convolution closed infinitely divisible models -- additive outliers -- Bayesian framework -- MCMC -- time series of counts -- state space models; Time-series analysis -- Periodicals; 519.232
• DOI: 10.1111/jtsa.12439
• Languages: English
• ISSNs: 0143-9782

Abstract

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.