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arXiv · 2408.09634

Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models

Brian Knaeble·R. Mitchell Hughes·George Rudolph·Mark A. Abramson·Daniel Razo
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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.

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Brian Knaeble, R. Mitchell Hughes, George Rudolph, Mark A. Abramson, Daniel Razo. 2024-08-19. Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models. https://arxiv.org/abs/2408.09634

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