Variable selection in sparse multivariate GLARMA models: Application to germination control by environment
稀疏多变量 GLARMA 模型的变量选择:环境对种子萌发调控的应用
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…