论文标题
稀疏Glarma模型中的可变选择
Variable selection in sparse GLARMA models
论文作者
论文摘要
在本文中，我们为稀疏的Glarma模型提出了一种新颖有效的两阶段变量选择方法，该方法无处不在，用于对离散值的时间序列进行建模。我们的方法是迭代地结合了GLARMA模型的自回归移动平均值（ARMA）系数的估计，并设计用于在广义线性模型（GLM）回归系数中执行可变选择的正则方法。我们首先在特定情况下建立ARMA部件系数估计器的一致性。然后，我们解释了如何有效地实施我们的方法。最后，我们使用合成数据评估方法的性能，将其与替代方法进行比较，并在现实世界应用的示例中进行说明。我们的方法是在Glarmavarsel R软件包中实施的，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，并且能够在系数估计方面胜过其他方法，尤其是在恢复非零回归系数方面。
In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling 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 coefficient estimators in a specific case. Then, we explain how to efficiently implement our approach. Finally, we assess the performance of our methodology using synthetic data, compare it with alternative methods and illustrate it on an example of real-world application. Our approach, which is implemented in the GlarmaVarSel 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 in terms of coefficient estimation, particularly in recovering the non null regression coefficients.
