Kernel PCA for multivariate extremes

no code implementations • 23 Nov 2022 • 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 reduction.

Dimensionality Reduction

Spectral learning of multivariate extremes

no code implementations • 15 Nov 2021 • Marco Avella Medina, Richard A. Davis, Gennady Samorodnitsky

We propose a spectral clustering algorithm for analyzing the dependence structure of multivariate extremes.

Clustering

Modeling of time series using random forests: theoretical developments

no code implementations • 6 Aug 2020 • Richard A. Davis, Mikkel S. Nielsen

In this paper we study asymptotic properties of random forests within the framework of nonlinear time series modeling.

regression Time Series +1

Indirect Inference for Time Series Using the Empirical Characteristic Function and Control Variates

no code implementations • 17 Apr 2019 • Richard A. Davis, Thiago do Rêgo Sousa, Claudia Klüppelberg

Motivated by Indirect Inference, we use a Monte Carlo approximation of the characteristic function based on iid simulated blocks.

Time Series Analysis Statistics Theory Statistics Theory 62F12, 62G20, 62M10, 65C05, 91G70