High-dimensional Cost-constrained Regression via Nonconvex Optimization.

ABSTRACT: Budget constraints become an important consideration in modern predictive modeling due to the high cost of collecting certain predictors. This motivates us to develop cost-constrained predictive modeling methods. In this paper, we study a new high-dimensional cost-constrained linear regression problem, that is, we aim to find the cost-constrained regression model with the smallest expected prediction error among all models satisfying a budget constraint. The non-convex budget constraint makes this problem NP-hard. In order to estimate the regression coefficient vector of the cost-constrained regression model, we propose a new discrete first-order continuous optimization method. In particular, our method delivers a series of estimates of the regression coefficient vector by solving a sequen

SUBMITTER: Yu G 

PROVIDER: S-EPMC9605684 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature