Do., 07. Juni 2018
14:00 - 15:00 Uhr, HG 217
Am 07. Juni wird Prof. Dr. Roland Fried von der Technischen Universität Dortmund einen Gastvortrag zum Thema "Robust time series analysis – the R-package robts" halten.
Many traditional methods of time series analysis are based on conditional least squares, Gaussian likelihood or empirical sample autocorrelations. Estimators and tests arising from these concepts can easily be misled by outliers and are not optimal in case of heavy-tailed distributions. Robust methods have been suggested in the literature therefore, particularly within the framework of linear time series modeling using, e.g., autoregressive moving average models. However, to the best of our knowledge, open-source software on such methods is scarce. Our R-package robts aims at filling this gap and provides functions which resemble the standard functions commonly used for analyzing autocorrelation or partial autocorrelation functions, for fitting autoregressive moving average time series models, for prediction and spectral density estimation. To achieve robustness we use M-estimators with bounded psi-functions instead of least squares or Gaussian likelihoods, and robust covariance and correlation estimators instead of ordinary sample autocorrelations. Change-point tests based on robust estimators allow us to check the basic assumption of 2nd order stationarity. The presentation illustrates the methods and our implementations using real data examples and studies their behavior via simulations. Our R-package comprises methods and techniques proposed by several authors including Maronna et al. . A review on robust estimators of autocorrelations and partial autocorrelations underlying our R-package can be found in Dürre et al. .
- Dürre, A., Fried, R. & Liboschik, T. (2015). Robust estimation of (partial) autocorrelation. WIREs Comput. Stat. 7, 205–222.
- Maronna, R., Martin, D. & Yohai, V. (2006). Robust Statistics: Theory and Methods. John Wiley & Sons, Chichester.