Rank-based change-point analysis for long-range dependent time series
Author:
Affiliation:
1. Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
2. Faculty of Mathematics, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
Publisher
Bernoulli Society for Mathematical Statistics and Probability
Subject
Statistics and Probability
Reference56 articles.
1. Feller, W. (1968). An Introduction to Probability Theory and Its Applications. Vol. I, 3rd ed. New York: Wiley.
2. Bai, S. and Taqqu, M.S. (2017). On the validity of resampling methods under long memory. Ann. Statist. 45 2365–2399. 10.1214/16-AOS1524
3. Bai, S. and Taqqu, M.S. (2019). Sensitivity of the Hermite rank. Stochastic Process. Appl. 129 822–840. 10.1016/j.spa.2018.03.020
4. Beran, J., Feng, Y., Ghosh, S. and Kulik, R. (2013). Long-Memory Processes: Probabilistic Properties and Statistical Methods. Heidelberg: Springer. 10.1007/978-3-642-35512-7
5. Beran, J., Möhrle, S. and Ghosh, S. (2016). Testing for Hermite rank in Gaussian subordination processes. J. Comput. Graph. Statist. 25 917–934. 10.1080/10618600.2015.1056345
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1. Rank-based change-point analysis for long-range dependent time series;Bernoulli;2022-11-01
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