Mean and Covariance Matrix Estimation under Heavy Tails
-
Updated
May 24, 2023 - R
Mean and Covariance Matrix Estimation under Heavy Tails
LambertW R package: Lambert W x F distributions and Gaussianization for skewed & heavy-tailed data
pylambertw - sklearn interface to analyze and gaussianize heavy-tailed, skewed data
Accuracy and performance benchmark of stable ("fat-tailed") distribution libraries in Python.
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards
Robust estimation using heavy-tailed distributions
Heavy-tailed ESD without gradient noise
Efficiently generate Gaussian stochastic processes with heavy-tailed algebraic correlations.
Heavy-Tailed distributions in Variational Autoencoder (VAE)
Adaptive Location and Scale Estimation with Kernel Weighted Averages - Technical Appendix and Supplemental R Code for Pokojovy et al (2024) CSSC Paper
Stochastc copula models for VaR and CVaR risk assessment
Add a description, image, and links to the heavy-tailed-distributions topic page so that developers can more easily learn about it.
To associate your repository with the heavy-tailed-distributions topic, visit your repo's landing page and select "manage topics."