arXiv (All)
open access
High-dimensional Gaussian Graphical Model Testing for Long-Memory Time Series
Many real-world high-dimensional time series exhibit long-memory, but Gaussian graphical model testing in this regime remains understudied. We develop a direct, data-adaptive test statistic for assessing conditional independence in the graph structure of stationary Gaussian time series. We establish
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs#974582
- A Review of Accelerated Test Models#168049
- SuperARC: a test for artificial superintelligence based on compressed modelling, recursive prediction and problem complexity.#262946
- A Two-Sample Test for Random Persistence Diagrams Against Alternatives Characterized by Persistence Intensity Differences#987758
- Limit theorems for the one-dimensional parabolic Anderson model with white noise potential#1033753
Record · ID 1104764
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive