On Outliers and Interventions in Count Time Series following GLMs

Austrian Journal of Statistics

article

Roland Fried¹  Tobias Liboschik¹  Hanan Elsaied²  Stella Kitromilidou³  Konstantinos Fokianos³ 

[1] TU Dortmund;Suez Canal Univ;Univ. of Cyprus

关键词: discrete data;    model identification;    robustness;    (partial) autocorrelations;    surveillance.;   

DOI  :  10.17713/ajs.v43i3.30

学科分类：医学（综合）

来源: Austrian Statistical Society

【 摘 要 】

We discuss the analysis of count time series following generalised linear models in the presence of outliers and intervention effects. Different modifications of such models are formulated which allow to incorporate, detect and to a certain degree distinguish extraordinary events (interventions) of different types in count time series retrospectively. An outlook on extensions to the problem of robust parameter estimation, identification of the model orders by robust estimation of autocorrelations and partial autocorrelations, and online surveillance by sequential testing for outlyingness is provided.

【 授权许可】

CC BY