Subseries Methods in Regression
将平稳序列的子序列方法扩展到回归中，通过子序列复制估计抽样分布，改进正态近似并减少稳健估计的偏差，模拟和空间数据应用验证了效果。
Abstract We extend subseries methods for stationary sequences to the regression setting. To estimate sampling distributions, the subseries approach computes the statistic of interest on all possible subseries of a shorter length than the original series, and uses the distribution of these replicates to mimic the distribution of the original statistic. For proper choice of subseries length, the regression parameters can be estimated to order O(n -2/3), thus improving on the normal approximation. The replicates can also be used to reduce the bias of robust estimators. Simulation results demonstrate the finite-sample effectiveness of the approach for both distribution function estimation and bias reduction. Applications to spatial data are also discussed.