Subbotin graphical models for extreme value dependencies with applications to functional neuronal connectivity

DOI10.1214/22-aoas1723arXiv2106.11554OpenAlexW4386519412MaRDI QIDQ6179133

Genevera I. Allen, Andersen Chang

Publication date: 16 January 2024

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2106.11554

zbMATH Keywords

extreme valuesgraphical modelscalcium imaginggeneralized normal distributionSubbotin distributionexponential family graphical models

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Regularized estimation in sparse high-dimensional time series models
• Sparse inverse covariance estimation with the graphical lasso
• A generalized normal distribution
• Exact Spike Train Inference Via $\ell_0$ Optimization
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• Multivariate \(\theta\)-generalized normal distributions
• The generalized extreme value distribution
• On the geometry of multivariate generalized Gaussian models
• Sparse regression for extreme values
• On model selection consistency of regularized M-estimators
• High-dimensional graphs and variable selection with the Lasso
• On Graphical Models via Univariate Exponential Family Distributions
• Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions
• The optimal L_pnorm estimator in linear regression models
• The linear regression model: L_pnorm estimation and the choice of p
• A multivariate generalization of the power exponential family of distributions
• Stability Selection
• Graphical Models for Extremes
• Structural Learning with Time-Varying Components: Tracking the Cross-Section of Financial Time Series

This page was built for publication: Subbotin graphical models for extreme value dependencies with applications to functional neuronal connectivity