Spatial Scale-Aware Tail Dependence Modeling for High-Dimensional Spatial Extremes

A 5.6 MB dataset from figshare supports a novel mixture model for high-dimensional spatial extremes, applied to approximately 600 spatial locations. Author Muyang Shi's research demonstrates nonstationary tail dependence in extreme summertime precipitation over the central United States. The model, which allows for both asymptotic dependence and independence at different spatial scales, was last updated on April 28, 2026.

Use Cases

• Modeling heterogeneous tail dependence in extreme spatial events based on the described mixture model.
• Assessing asymptotic dependence and independence in extreme precipitation based on the described spatial scale-aware framework.
• Performing high-dimensional Bayesian inference for spatial processes based on the described hierarchical copula approach.
• Simulating extreme event scenarios with nonstationary dependence structures based on the model's described capabilities.

Strengths

• Dataset size is 5.6 MB, indicating a manageable download.
• Model application covers approximately 600 spatial locations, enabling high-dimensional inference.
• Includes simulation results showing close to nominal frequentist coverage rates.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Row count and specific column definitions are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.6 MB size suggests the dataset is relatively small, which may limit the scope of analysis.

Provenance

Source

Muyang Shi via figshare

Collection Method

Likely derived from observational or simulated data for modeling extreme summertime precipitation.

Freshness

Last updated 2026-04-28 19:35:06

Geography

Central United States