Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Epprecht, Camila; Guégan, Dominique; Veiga, Álvaro; Correa da Rosa, Joel
• Is Part Of: Communications in statistics. Volume 50:Issue 1(2021)
• Page Start: 103
• Page End: 122
• Publication Date: 2021-01-02
• Subjects: Model selection -- General-to-specific -- Adaptive LASSO -- Sparse models -- Monte Carlo simulation -- Genetic data; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2018.1554104
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: In this article we compare two approaches of model selection methods for linear regression models: classical approach — Autometrics (automatic general-to-specific selection)—and statistical learning —LASSO ( ℓ 1 -norm regularization) and adaLASSO (adaptive LASSO). In a simulation experiment, considering a simple setup with orthogonal candidate variables and independent data, we compare the performance of the methods concerning predictive power (out-of-sample forecast), selection of the correct model (variable selection) and parameter estimation. The case where the number of candidate variables exceeds the number of observation is considered as well. Finally, in an application using genomic data from a high-throughput experiment we compare the predictive power of the methods to predict epidermal thickness in psoriatic patients, and we perform a simulation experiment with correlated variables, based on the application.