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Title: Robust change point tests using bounded transformations Authors:  Alexander Duerre - TU Dortmund (Germany) [presenting]
Roland Fried - TU Dortmund University (Germany)
Abstract: Classical moment based change point tests like the cusum test are very powerful under Gaussian time series with no more than one change point but behave poorly under heavy tailed distributions and corrupted data. A new class of robust change point tests based on cusum statistics of robustly transformed observations is proposed. This framework is very flexible, depending on the used transformation one can detect amongst others changes in the mean, scale or dependence of a possibly multivariate time series. The calculation of $p$-values can be simplified by using asymptotics which yields a computational complexity of $T log(T)$ where $T$ is the number of observations. We apply our general approach to detect changes in the covariance structure of a multivariate time series. Simulations indicate high power under Gaussianity as well as heavy tails.