Fast Cost-constrained Regression

Oregon Convention Center 

The conventional statistical models assume the availability of covariates without associated costs, yet real-world scenarios often involve acquisition costs and budget constraints imposed on these variables. Scientists must navigate a trade-off between model accuracy and expenditure within these constraints. In this paper, we introduce fast cost-constrained regression (FCR), designed to tackle such problems with computational and statistical efficiency. Specifically, we develop fast and efficient algorithms to solve cost-constrained problems with the loss function satisfying a quadratic majorization condition. We theoretically establish nonasymptotic error bounds for the algorithm's solution, considering both estimation and selection accuracy. We apply FCR to extensive numerical simulations and four datasets from the National Health and Nutrition Examination Survey. Our method outperforms the latest approaches in various performance measures, while requiring fewer iterations and a shorter runtime.

budget constraints

cost

high dimensional regression

non-convex optimiztion 

Biometrics Section