Multiscale Quantile Segmentation
Author:
Affiliation:
1. Institute for Mathematical Stochastics, University of Göttingen, Göttingen, Germany;
2. Department of Statistics, University of California at Berkeley, Berkeley, CA;
3. Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2020.1859380
Reference61 articles.
1. On computation of the running median
2. Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle
3. Piecewise quantile autoregressive modeling for nonstationary time series
4. Narrowest-over-threshold detection of multiple change points and change-point-like features
5. Multiscale blind source separation
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2. Optimal multiple change-point detection for high-dimensional data;Electronic Journal of Statistics;2023-01-01
3. Segmenting Time Series via Self-Normalisation;Journal of the Royal Statistical Society Series B: Statistical Methodology;2022-10-30
4. A Variational View on Statistical Multiscale Estimation;Annual Review of Statistics and Its Application;2022-03-07
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