arXiv (OAI Expanded)
open access
Multicollinearity-agnostic feature screening for non-Euclidean responses: a factor adjusted approach
In high-dimensional settings, multicollinearity is a pervasive issue that can substantially impair the performance of feature screening methods based on marginal Fréchet regression. Feature screening for non-Euclidean responses becomes unreliable when ultrahigh-dimensional predictors suffer from mul
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
- Resampling NANCOVA: Nonparametric Analysis of Covariance in Small Samples#1035273
- Bayesian Variable Selection for High-Dimensional Predictors with Missing Psychometric Outcomes#998597
- MMFNet: A multi-branch multi-scale framework with adaptive sparse self-attention and cross-modal fusion for sleep stage assessment.#380741
- Cheap and Powerful Tests for Supervised Subspaces: Per-Component Inference for PLS#1131149
- Multiple Linear Regression Based LOS Analysis in the General Medicine Department#185507
Record · ID 1084392
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