Sparse estimation of parameter support sets for generalized vector autoregressions by resampling and model aggregation
通过重抽样与模型聚合稀疏估计广义向量自回归的参数支撑集
The central problem we address in this work is estimation of the parameter support set S, the set of indices corresponding to nonzero parameters, in the context of a sparse parametric likelihood model for discrete multivariate time series. We develop an algorithm that performs the estimation by aggregating support sets obtained by applying the LASSO to data subsamples. Our approach is to identify several candidate models and estimate S by selecting common parameters, thus…