This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule-Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints $m$ and the series length $N$ satisfies $m/N \rightarrow 0$ as $N \rightarrow \infty$

翻译：本文章在出现数量未知的中值变化时研究固定的自动自动结构估计值。 在此, 提出并研究一个Yule- Walker时时空估计器, 用于受平均转移变化点污染的依附时间序列中受平均转移变化点污染的自动递减参数。 估计器基于该系列的第一顺序差异, 当更改点数为百万美元和序列数为美元/ n\rightror 0美元时, 该估计器被证明是一致和无症状的, 当更改点数为百万美元和序列长度为N美元/ n\rightror 0美元时, 则该估计器以该序列的顺序差异为基础, 并且当更改点数为美元/ n\rightrowle\ infty $时, 被证明是一致和无症状的正常。