A Cross-Validatory Method for Dependent Data
将交叉验证扩展到一般平稳序列数据，提出h块交叉验证方法，通过移除测试集观测前后h个观测来减少训练集，并加入补偿项解决样本利用不足问题，模拟显示优于传统方法。
In this paper we extend the technique of cross-validation to the case where observations form a general stationary sequence. We call it h-block cross-validation, because the idea is to reduce the training set by removing the h observations preceding and following the observation in the test set. We propose taking h to be a fixed fraction of the sample size, and we add a term to our h-block cross-validated estimate to compensate for the underuse of the sample. The advantages of the proposed modification over the cross-validation technique are demonstrated via simulation.