影响因子：0.0
DOI码：10.3969/j.issn.1001-4268.2020.05.005
发表刊物：Chinese Journal of Applied Probability and Statistics
关键字：change point; least-square method; consistency; convergence rate; limiting distribution
摘要：When the sample size is $N$, the computational complexity of the least squares estimate of mean change point is $O\left( {{N^2}} \right)$, and it's necessary to reduce the computational complexity in the case of huge data. In this paper, a two-stage fast scanning algorithm is proposed for the estimation of mean change point, and it is proved that this method has the same convergence speed and limiting distribution as the least squares estimation of mean change point, and the optimal complexity of the new algorithm is $O\left( {{N^{4/3}} \cdot b_n^{2/3}} \right)$. We have conducted sufficient data experiments in terms of computation time and estimated efficiency, and the results show that the estimated efficiency of the new and old methods is similar,but the computation time of our method is obviously shortened.
论文类型：期刊论文
卷号：36
期号：5
页面范围：493-508
ISSN号：1001-4268
是否译文：否
第一作者：Ping Cao
通讯作者：Zhiming Xia