Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models
Methodology
2024-08-20 v1 Machine Learning
Optimization and Control
Abstract
It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the context of high-dimensional data, there are too many model extensions to check. However, as we show here, it is possible to efficiently search, with a branch and bound algorithm, for maximum and minimum values of that adjusted slope coefficient over a discrete space of regularized regression models. Here we introduce our algorithm, along with supporting mathematical results, an example application, and a link to our computer code, to help researchers summarize high-dimensional data and assess the stability of regression coefficients in uncertain models.
Keywords
Cite
@article{arxiv.2408.09634,
title = {Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models},
author = {Brian Knaeble and R. Mitchell Hughes and George Rudolph and Mark A. Abramson and Daniel Razo},
journal= {arXiv preprint arXiv:2408.09634},
year = {2024}
}