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Contributions to the Lasso method and extensions

Grant number: 20/16334-9
Support Opportunities:Scholarships abroad - Research
Effective date (Start): January 10, 2022
Effective date (End): January 09, 2023
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Statistics
Principal Investigator:Gustavo Henrique de Araujo Pereira
Grantee:Gustavo Henrique de Araujo Pereira
Host Investigator: Jianwen Cai
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Research place: University of North Carolina at Chapel Hill (UNC), United States  

Abstract

Lasso is a widely used method for parameter estimation in parametric regression models. Part of its success lies on the fact that it gives better predictive power than traditional methods such as least squares or maximum likelihood. In addition, Lasso still uses the easy-to-interpret components of parametric regression models. However, it is often the case that Lasso has worse predictive power than non-parametric regression models, which are usually hard to interpret. This project proposes modifications in Lasso or in one of the extensions of the method so that its predictive power gets closer to the best non-parametric models. Our methods will be especially useful in settings where one wants to use the estimated regression to get accurate predictions as well as to get explanations. (AU)

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