Autori
Gomtsyan, Marina
Lévy-Leduc, Céline
Ouadah, Sarah
Sansonnet, Laure
Bailly, Christophe
Rajjou, Loï,c

Titolo
Variable selection in sparse multivariate GLARMA models: application to germination control by environment
Periodico
Statistical methods & applications : Journal of the Italian Statistical Society
Anno: 2025 - Volume: 34 - Fascicolo: 2 - Pagina iniziale: 291 - Pagina finale: 324

We propose an iterative variable selection approach in multivariate sparse GLARMA models for modeling multivariate discrete-valued time series. The estimation in our approach is performed in two steps: firstly, our approach estimates the autoregressive moving average (ARMA) coefficients of multivariate GLARMA models, followed by variable selection in the coefficients of the Generalized Linear Model using regularized methods. We provide a detailed description of the implementation of our approach. Subsequently, we study its performance on simulated data and compare it with other methods. Finally, we illustrate its application on RNA-Seq data resulting from polyribosome profiling to determine translational status for all mRNAs in germinating seeds. The proposed approach benefits from a number of attractive features: it has a low computational load and outperforms other methods in accurately performing variable selection and, consequently, recovering the null and non-null coefficients. Furthermore, being implemented in the MultiGlarmaVarSel R package and openly accessible on the CRAN, our variable selection method holds significant appeal for broader applications across diverse scientific disciplines.



SICI: 1618-2510(2025)34:2<291:VSISMG>2.0.ZU;2-Q

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