# Variable selection in sparse high-dimensional GLARMA models

@article{LevyLeduc2019VariableSI, title={Variable selection in sparse high-dimensional GLARMA models}, author={C'eline L'evy-Leduc and Sarah Ouadah and Laure Sansonnet}, journal={arXiv: Statistics Theory}, year={2019} }

In this paper, we propose a novel variable selection approach in the framework of sparse high-dimensional GLARMA models. It consists in combining the estimation of the moving average (MA) coefficients of these models with regularized methods designed for Generalized Linear Models (GLM). The properties of our approach are investigated both from a theoretical and a numerical point of view. More precisely, we establish in some specific cases the consistency of the MA part coefficient estimators…

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