Variable selection in sparse GLARMA models

@article{Gomtsyan2020VariableSI,
  title={Variable selection in sparse GLARMA models},
  author={Marina Gomtsyan and C{\'e}line L{\'e}vy-Leduc and Sarah Ouadah and Laure Sansonnet and Thomas Blein},
  journal={Statistics},
  year={2020},
  volume={56},
  pages={755 - 784}
}
In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modelling discrete-valued time series. Our approach consists in iteratively combining the estimation of the autoregressive moving average (ARMA) coefficients of GLARMA models with regularized methods designed for performing variable selection in regression coefficients of Generalized Linear Models (GLM). We first establish the consistency of the ARMA part… 

Variable selection in sparse multivariate GLARMA models: Application to germination control by environment

This work proposes a novel and eficient iterative two-stage variable selection approach for multivariate sparse GLARMA models, which can be used for modelling multivariate discrete-valued time series and is able to outperform the other methods for recovering the null and non-null coefficients.

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