A0402
Title: Outlier-robust estimation of state-space models using a penalized approach
Authors: Rajan Shankar - University of Sydney (Australia) [presenting]
Garth Tarr - University of Sydney (Australia)
Ines Wilms - Maastricht University (Netherlands)
Jakob Raymaekers - University of Antwerp (Belgium)
Abstract: State-space models are a broad class of statistical models for time-varying data. The Gaussian distributional assumption on the disturbances in the model leads to poor parameter estimates in the presence of additive outliers. Whilst there are ways to mitigate the influence of outliers via traditional robust estimation methods such as M-estimation, this issue is approached from a more modern perspective that utilizes penalization. A shift parameter is introduced at each timepoint, with the goal being that outliers receive a non-zero shift parameter while clean timepoints receive a zero shift parameter after estimation. The vector of shift parameters is penalized to ensure that not all shift parameters are trivially non-zero. Apart from making it feasible to fit accurate and reliable time series models in the presence of additive outliers, other benefits of this approach include automatic outlier flagging and visual diagnostic tools such as BIC curves to provide researchers and practitioners with better insights into the outlier structure of the data.
