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Bootstrap Change Point Testing for Dependent Data

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Statistics and Simulation (IWS 2015)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 231))

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Abstract

Critical values of change point tests in location and regression models are usually based on limit distribution of the respective test statistics under the null hypothesis. However, the limit distribution is very often a functional of some Gaussian processes depending on unknown quantities that cannot be easily estimated. In many situations, convergence to the asymptotic distribution is rather slow and the asymptotic critical values are not well applicable in small and moderate samples. It has appeared that resampling methods provide reasonable approximations for critical values of test statistics for detection changes in location and regression models. In this chapter dependent wild bootstrap procedure for testing changes in linear model with weakly dependent regressors and errors will be proposed and its validity verified. More specifically, the concept of \(L_p\)-m-approximability will be used.

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Acknowledgements

Research supported by the Czech Science Foundation project GA15-09663S.

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Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University, Sokolovská 83, Prague, Czech Republic

    Zuzana Prášková

Authors
  1. Zuzana Prášková
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Correspondence to Zuzana Prášková .

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Editors and Affiliations

  1. Institute of Statistics, Alpen-Adria University of Klagenfurt, Klagenfurt, Austria

    Jürgen Pilz

  2. Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Vienna, Austria

    Dieter Rasch

  3. St. Petersburg State University, St. Petersburg, Russia

    Viatcheslav B. Melas

  4. Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Vienna, Austria

    Karl Moder

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Prášková, Z. (2018). Bootstrap Change Point Testing for Dependent Data. In: Pilz, J., Rasch, D., Melas, V., Moder, K. (eds) Statistics and Simulation. IWS 2015. Springer Proceedings in Mathematics & Statistics, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-319-76035-3_4

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