Cover Image

Graph structure learning for stable processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Brück, Florian, Engelke, Sebastian, Volgushev, Stanislav
Format:	Preprint
Published:	2026
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning extremal graphical structures in high dimensions
by: Engelke, Sebastian, et al.
Published: (2021)

Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data
by: Yu, Lu, et al.
Published: (2022)

Simultaneous hypothesis testing for comparing many functional means
by: Decker, Colin, et al.
Published: (2025)

Infinitely divisible priors for multivariate survival functions
by: Brück, Florian
Published: (2025)

Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
by: Zhang, Xinyi, et al.
Published: (2022)

Generative neural networks for characteristic functions
by: Brück, Florian
Published: (2024)

Extremes of structural causal models
by: Engelke, Sebastian, et al.
Published: (2025)

Progression: an extrapolation principle for regression
by: Buriticá, Gloria, et al.
Published: (2024)

Extremal graphical modeling with latent variables via convex optimization
by: Engelke, Sebastian, et al.
Published: (2024)

Extremal Random Forests
by: Gnecco, Nicola, et al.
Published: (2022)

Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
by: Pasche, Olivier C., et al.
Published: (2022)

Distribution free MMD tests for model selection with estimated parameters
by: Brück, Florian, et al.
Published: (2023)

A Kullback-Leibler divergence test for multivariate extremes: theory and practice
by: Engelke, Sebastian, et al.
Published: (2026)

Lévy graphical models
by: Engelke, Sebastian, et al.
Published: (2024)

Extrapolation in Statistical Learning with Extreme Value Theory
by: Engelke, Sebastian, et al.
Published: (2026)

Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
by: Pasche, Olivier C., et al.
Published: (2025)

Generative machine learning methods for multivariate ensemble post-processing
by: Chen, Jieyu, et al.
Published: (2022)

Bayesian inference of sparsity in stable vector autoregressive processes
by: Heaps, Sarah E., et al.
Published: (2026)

Graphical models for multivariate extremes
by: Engelke, Sebastian, et al.
Published: (2024)

Graphical models for infinite measures with applications to extremes
by: Engelke, Sebastian, et al.
Published: (2022)

Guided structure learning of DAGs for count data
by: Nguyen, Thi Kim Hue, et al.
Published: (2022)

Unguided structure learning of DAGs for count data
by: Nguyen, Thi Kim Hue, et al.
Published: (2024)

Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes
by: Huser, Raphaël, et al.
Published: (2024)

Intrinsic Whittle--Matérn fields and sparse spatial extremes
by: Bolin, David, et al.
Published: (2025)

Disentangling the structure of ecological bipartite networks from observation processes
by: Anakok, Emre, et al.
Published: (2022)

Sparsity learning via structured functional factor augmentation
by: Ma, Hanteng, et al.
Published: (2025)

Gaussian process priors with Markov properties for effective reproduction number inference
by: Sebastian, Jessalyn N., et al.
Published: (2025)

Adaptivity of the NPMLE to finitely discrete mixing distributions in Gaussian/Poisson mixtures
by: Zhang, Yan, et al.
Published: (2026)

On a surprising behavior of the likelihood ratio test in non-parametric mixture models
by: Zhang, Yan, et al.
Published: (2025)

Online Bayesian changepoint detection for network Poisson processes with community structure
by: Corneck, Joshua, et al.
Published: (2024)

Theoretical properties of angular halfspace depth
by: Nagy, Stanislav, et al.
Published: (2024)

Robust Functional Regression with Discretely Sampled Predictors
by: Kalogridis, Ioannis, et al.
Published: (2023)

Density regression via Dirichlet process mixtures of normal structured additive regression models
by: Rodríguez-Álvarez, María Xosé, et al.
Published: (2024)

Which depth to use to construct functional boxplots?
by: Nagy, Stanislav, et al.
Published: (2024)

Valid and efficient possibilistic structure learning in Gaussian linear regression
by: Martin, Ryan, et al.
Published: (2025)

Approximation of bivariate densities with compositional splines
by: Škorňa, Stanislav, et al.
Published: (2024)

A stableness of resistance model for nonresponse adjustment with callback data
by: Miao, Wang, et al.
Published: (2021)

High Rank Path Development: an approach of learning the filtration of stochastic processes
by: Tao, Jiajie, et al.
Published: (2024)

A structural equation formulation for general quasi-periodic Gaussian processes
by: Nigam, Unnati, et al.
Published: (2025)

Non-stationary max-stable models with an application to heavy rainfall data
by: Forster, Carolin, et al.
Published: (2022)