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Flexibility and Robustness from ROBUST

Banff International Research Station for Mathematical Innovation and Discovery

Flexibility and robustness are quite often two important aspects to keep in mind when dealing with some regression model estimation: the model should be flexible to adapt for the underlying structure which we want to estimate and, on the other hand, we would like to stay free of any complicated and unrealistic assumptions. There are of course many different approaches on how to try to achieve both. In our work we focus on change-point detection and estimation in nonparametric regression models while allowing for heavy tailed distributions of random errors and even dependent observations. In order to decide whether some change-point is statistically relevant for the model or not, we also introduce a statistical test which can be used to draw a proper decision. Finally, we present some examples to show that, indeed, robustness and flexibility can play a crucial role in the regression estimation.

Mathematics; Statistics

video/mp4

Author affiliation: Charles University

2017-03-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/60792

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/