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<p>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 <Emphasis FontCategory="NonProportional">MultiGlarmaVarSel</Emphasis> R package and openly accessible on the CRAN, our variable selection method holds significant appeal for broader applications across diverse scientific disciplines.</p>

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

  • 期刊论文

  • 发表日期：

    2025年4月29日

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  • Marina Gomtsyan,
  • Céline Lévy-Leduc,
  • Sarah Ouadah,
  • Laure Sansonnet,
  • Christophe Bailly,
  • Loïc Rajjou

摘要

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.