论文标题
稀疏多元GLARMA模型中的可变选择：通过环境应用发芽控制
Variable selection in sparse multivariate GLARMA models: Application to germination control by environment
论文作者
论文摘要
我们为多元稀疏GLARMA模型提出了一种新颖而有效的迭代两阶段变量选择方法，该方法可用于建模多元离散值时间序列。我们的方法包括迭代结合两个步骤：多变量GLARMA模型的自回归运动平均值（ARMA）系数的估计以及通过正则化方法执行的通用线性模型（GLM）部分系数中的变量选择。我们解释了如何有效地实施我们的方法。然后，我们使用合成数据评估方法的性能，并将其与替代方法进行比较。最后，我们在多核糖体分析引起的RNA-seq数据上进行了说明，以确定所有在发芽种子中所有mRNA的翻译状态。我们的方法是在Multiglarmavarsel R软件包中实现的，并且在Cran上可用，它非常有吸引力，因为它受益于低计算负载，并且能够优于恢复零和非零系数的其他方法。
We propose a novel and efficient iterative two-stage variable selection approach for multivariate sparse GLARMA models, which can be used for modelling multivariate discrete-valued time series. Our approach consists in iteratively combining two steps: the estimation of the autoregressive moving average (ARMA) coefficients of multivariate GLARMA models and the variable selection in the coefficients of the Generalized Linear Model (GLM) part of the model performed by regularized methods. We explain how to implement our approach efficiently. Then we assess the performance of our methodology using synthetic data and compare it with alternative methods. Finally, we illustrate it on RNA-Seq data resulting from polyribosome profiling to determine translational status for all mRNAs in germinating seeds. Our approach, which is implemented in the MultiGlarmaVarSel R package and available on the CRAN, is very attractive since it benefits from a low computational load and is able to outperform the other methods for recovering the null and non-null coefficients.
