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stat.ML2022★ 1 cited
Kernel PCA for multivariate extremes
Marco Avella-Medina, Richard A. Davis, Gennady Samorodnitsky
We propose kernel PCA as a method for analyzing the dependence structure of multivariate extremes and demonstrate that it can be a powerful tool for clustering and dimension reduct…
stat.ML2020
Modeling of time series using random forests: theoretical developments
Richard A. Davis, Mikkel S. Nielsen
In this paper we study asymptotic properties of random forests within the framework of nonlinear time series modeling. While random forests have been successfully applied in variou…