A Parameter-Centric View on Regression

2026-04-19

AI Summary

This study addresses the core assumptions and limitations inherent in “regression via composition” approaches by proposing a novel parameter-centric perspective on regression analysis. Departing from conventional data- or function-centered paradigms, this framework places model parameters at the heart of the modeling process, integrating statistical modeling, causal inference, and parametric analysis to systematically reconstruct the theoretical foundations of regression. The work demonstrates that a parameter-centric viewpoint enhances model interpretability and structural robustness, while also offering a principled basis for developing regression models with stronger causal grounding and improved predictive performance. By reframing regression through the lens of parameterization, the study provides both theoretical justification and practical pathways for advancing the design of more meaningful and effective regression methodologies.

Technology Category

• Machine Learning: Classification and Regression
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Discussion on ``Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt

Problem

• regression
• parameter-centric
• causal inference
• modeling

Innovation

• parameter-centric
• regression by composition
• causal inference
• model interpretability
• statistical modeling