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Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics

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  • Camila Epprecht

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PUC-Rio - Pontifícia Universidade Católica do Rio de Janeiro [Brasil] = Pontifical Catholic University of Rio de Janeiro [Brazil] = Université catholique pontificale de Rio de Janeiro [Brésil])

  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Álvaro Veiga

    (PUC-Rio - Pontifícia Universidade Católica do Rio de Janeiro [Brasil] = Pontifical Catholic University of Rio de Janeiro [Brazil] = Université catholique pontificale de Rio de Janeiro [Brésil])

  • Joel Correa da Rosa

    (MSSM - Icahn School of Medicine at Mount Sinai [New York])

Abstract

In this paper we compare two approaches of model selection methods for linear regression models: classical approach - Autometrics (automatic general-to-specific selection) — and statistical learning - LASSO (ℓ1-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 highthroughput experiment we compare the predictive power of the methods to predict epidermal thickness in psoriatic patients.

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  • Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917797, HAL.
    • Handle: RePEc:hal:cesptp:halshs-00917797
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00917797v2
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    References listed on IDEAS

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    Keywords

    model selection; general-to-specific; adaptive LASSO; sparse models; Monte Carlo simulation; genetic data;
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