@misc{10498/30592,
year = {2021},
url = {http://hdl.handle.net/10498/30592},
abstract = {The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full control over the accuracy prediction on certain individ- uals of interest is allowed.
In this work we propose a novel version of the Lasso in which quadratic performance con- straints are added to Lasso-based objective functions, in such a way that threshold values are set to bound the prediction errors in the different groups of interest (not necessarily disjoint). As a result, a constrained sparse regression model is defined by a nonlinear optimization prob- lem. This cost-sensitive constrained Lasso has a direct application in heterogeneous samples where data are collected from distinct sources, as it is standard in many biomedical contexts. Both theoretical properties and empirical studies concerning the new method are explored in this paper. In addition, two illustrations of the method on biomedical and sociological contexts are considered.},
publisher = {Springer},
keywords = {Performance Constraints},
keywords = {Cost-Sensitive Learning},
keywords = {Sparse Solutions},
keywords = {Sample Average Approximation},
keywords = {Heterogeneity},
keywords = {Lasso},
title = {A cost-sensitive constrained Lasso},
doi = {10.1007/S11634-020-00389-5},
author = {Blanquero, Rafael and Carrizosa, Emilio and Ramírez Cobo, Josefa and Sillero-Denamiel, M. Remedios},
}