高維度時間序列並帶有測量誤差模型之模型選擇

Abstract

We use a fast stepwise regression method, called orthogonal greedy algorithm (OGA) to select variables for high-dimensional time series model with measurement errors. Under a weak sparsity condition, we derive a convergence rate of OGA, which is expressed in terms of the number of iterations, the sample size and the order of the moment imposed on the error process. Under a strong sparsity condition, we develop a consistent model selection procedure using OGA and a high-dimensional information criterion.