arXiv (All)
open access
Lorentz Hyperbolic Weighted Regression: Theory for Fixed and Estimated Representations
Many applications provide each observation with a meaningful representation in addition to an ordinary response and covariates. When that representation is hierarchical, hub-periphery structured, or network derived, Euclidean or geographic proximity may define the wrong peer groups. We present Loren
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
- FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets#485173
- FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets.#305322
- A Regression-Based Framework for the ACF, PACF, Durbin-Levinson Recursion, and One-Step-Ahead Prediction#635959
- A Regression-Based Framework for the ACF, PACF, Durbin-Levinson Recursion, and One-Step-Ahead Prediction#633610
- HyCoSeq: Contextual Hyperbolic Representation Learning for Genomic Sequences#1123337
Record · ID 1048380
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