Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions
2026-05-06T07:24:04Z•5924aeb9cafef19c8cffdca5d34ba2f37b397b04a91ffd035897049b4f8df350
Fisher-discriminantPEIDPITStiefel-orthogonalityarxivcapacity-constraintscausal-inferenceconformal-predictioncopuladynamic-vine-copulaseffective-sample-sizehigher-order-interactionsimbalanced-classificationintrinsic-ESSkernel-discrepancymanifold-MCMCmultilabel-LDAnonstationarity','T-estimation'offline-contextual-MDPpercentile-intervalprobability-integral-transformresearch-collectionstat-mlsynergistic-causalitytail-dependence
What happened
RSS feed of new arXiv stat.ML submissions (2026-05-06). Contains multiple research contributions: Dynamic Vine Copulas (DVC) for detecting time-varying higher-order/conditional dependence beyond correlations; Conformalized Percentile Interval using PIT-based calibration for improved finite-sample and conditional coverage; an intrinsic effective sample size for manifold-valued MCMC via kernel discrepancy; Partial Effective Information Decomposition (PEID) for quantifying synergistic causality under interventions; theoretical analysis of orthogonal multilabel Fisher discriminants with Stiefel-od
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 5924aeb9cafef19c8cffdca5d34ba2f37b397b04a91ffd035897049b4f8df350
- Enrichment time
- 2026-05-06T07:24:04Z
- AI-assisted enrichment
- Yes
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