Doubly sparse regression incorporating graphical structure among predictors

• Autores: Matthew Stephenson, R Ayesha Ali, Gerarda A Darlington
• Localización: Canadian Journal of Statistics = Revue Canadienne de Statistique, ISSN 0319-5724, Vol. 47, Nº. 4, 2019, págs. 729-747
• Idioma: inglés
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• Resumen

  • Recent research has demonstrated that information learned from building a graphical model on the predictor set of a regularized linear regression model can be leveraged to improve prediction of a continuous outcome. In this article, we present a new model that encourages sparsity at both the level of the regression coefficients and the level of individual contributions in a decomposed representation. This model provides parameter estimates with a finite sample error bound and exhibits robustness to errors in the input graph structure. Through a simulation study and the analysis of two real data sets, we demonstrate that our model provides a predictive benefit when compared to previously proposed models. Furthermore, it is a highly flexible model that provides a unified framework for the fitting of many commonly used regularized regression models. The Canadian Journal of Statistics 47: 729–747; 2019 © 2019 Statistical Society of Canada