Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models
<|reference_start|>Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models: 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.<|reference_end|>
@article{knaeble2024branch,
    title={Branch and Bound to Assess Stability of Regression Coefficients in
  Uncertain Models},
    author={Brian Knaeble, R. Mitchell Hughes, George Rudolph, Mark A. Abramson,
  Daniel Razo},
    journal={arXiv preprint arXiv:2408.09634},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09634},
    primaryClass={stat.ME cs.LG math.OC}
}