Optimal change-point estimation in time series
研究了α混合条件下时间序列变点的最优估计渐近理论，证明了贝叶斯型估计量在平方误差损失下渐近极小极大，并开发了两种自助法构建置信区间。
This paper establishes asymptotic theory for optimal estimation of change points in general time series models under α-mixing conditions. We show that the Bayes-type estimator is asymptotically minimax for change-point estimation under squared error loss. Two bootstrap procedures are developed to construct confidence intervals for the change points. An approximate limiting distribution of the change-point estimator under small change is also derived. Simulations and real data applications are presented to investigate the finite sample performance of the Bayes-type estimator and the bootstrap procedures.