arXiv (All)
open access
Conformalized Regression for Continuous Bounded Outcomes
Regression problems with continuous bounded outcomes frequently arise in statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in this setting is predicting the response at a new covariate value. Most of the existing literature has focused
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
- Rolling Conformal Prediction in Sequential Model Training#1063162
- Reconciling Interpretability with Covariate-Dependent Shape Flexibility in Penalized Transformation Models for Distributional Regression#1099920
- Learning with Synthetic Data via SGD in High-Dimensional Linear Regression#987308
- Fitted Q-Evaluation without Bellman Completeness via Occupancy Weighting#798710
- Fitted Q-Evaluation without Bellman Completeness via Occupancy Weighting#797296
Record · ID 1144721
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