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

Marina Gomtsyan (), Céline Lévy-Leduc, Sarah Ouadah, Laure Sansonnet, Christophe Bailly and Loïc Rajjou
Additional contact information
Marina Gomtsyan: Université Paris Cité and Sorbonne Université, CNRS
Céline Lévy-Leduc: Université Paris Cité and Sorbonne Université, CNRS
Sarah Ouadah: Sorbonne Université and Université Paris Cité, CNRS
Laure Sansonnet: Sorbonne Université and Université Paris Cité, CNRS
Christophe Bailly: Sorbonne Université, CNRS
Loïc Rajjou: Université Paris-Saclay

Statistical Methods & Applications, 2025, vol. 34, issue 2, No 6, 324 pages

Abstract: Abstract 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.

Keywords: Multivariate GLARMA; Sparsity; Variable selection; Seed quality; Gene expression (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-025-00786-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:34:y:2025:i:2:d:10.1007_s10260-025-00786-0

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-025-00786-0

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().