研究随机变量序列的部分和之和Tn=sum from i=1 to n(Si)(其中Sn=sum from i=1 to n(Xi))的极限性质,对强平稳NA序列,且EXi=0的条件下,获得了ETn2的稳定公式,并在此基础上,研究了其中心极限定理成立的条件,最后得到强平稳NA序列Tn的中...研究随机变量序列的部分和之和Tn=sum from i=1 to n(Si)(其中Sn=sum from i=1 to n(Xi))的极限性质,对强平稳NA序列,且EXi=0的条件下,获得了ETn2的稳定公式,并在此基础上,研究了其中心极限定理成立的条件,最后得到强平稳NA序列Tn的中心极限定理.展开更多
In this paper,we investigate the CUSUM statistic of change point under the neg-atively associated(NA)sequences.By establishing the consistency estimators for mean and covariance functions respectively,the limit distri...In this paper,we investigate the CUSUM statistic of change point under the neg-atively associated(NA)sequences.By establishing the consistency estimators for mean and covariance functions respectively,the limit distribution of the CUSUM statistic is proved to be a standard Brownian bridge,which extends the results obtained under the case of an indepen-dent normal sample and the moving average processes.Finally,the finite sample properties of the CUSUM statistic are given to show the efficiency of the method by simulation studies and an application on a real data analysis.展开更多
文摘研究随机变量序列的部分和之和Tn=sum from i=1 to n(Si)(其中Sn=sum from i=1 to n(Xi))的极限性质,对强平稳NA序列,且EXi=0的条件下,获得了ETn2的稳定公式,并在此基础上,研究了其中心极限定理成立的条件,最后得到强平稳NA序列Tn的中心极限定理.
基金Supported by the NNSF of China(11701004,11801003)NSSF of China(14ATJ005)+1 种基金NSF of Anhui Province(1808085QA03,1808085QA17,1808085QF212,2008085MA14)Provincial Natural Science Research Project of Anhui Colleges(KJ2019A0006,KJ2019A0021).
文摘In this paper,we investigate the CUSUM statistic of change point under the neg-atively associated(NA)sequences.By establishing the consistency estimators for mean and covariance functions respectively,the limit distribution of the CUSUM statistic is proved to be a standard Brownian bridge,which extends the results obtained under the case of an indepen-dent normal sample and the moving average processes.Finally,the finite sample properties of the CUSUM statistic are given to show the efficiency of the method by simulation studies and an application on a real data analysis.