arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.07226math.PR
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关于高维时间序列块相关阵的eigenvalue分布的渐近性质

On the asymptotic behaviour of the eigenvalue distribution of block correlation matrices of high-dimensional time series

Philippe Loubaton, Xavier Mestre

摘要:

我们认为线性谱统计建立从一组$M$相互独立的标量时间序列的块归一化相关矩阵。该矩阵由$M \times M$块组成,$M \times M$块包含时间序列对之间的样本互相关。具体地,每个块具有大小$L \times L$,并且包含在每对时间序列之间的$L$连续时间滞后处测量的样本互相关。令$N$表示用于估计这些相关阵的连续观测窗口的总数。我们分析的渐近制度,其中$M,L,N \rightarrow +\infty$,而$ML/N \rightarrow c_\star$,$0<c_\star<\infty$。我们研究了线性统计的行为,这个块相关矩阵的特征值在这些渐近条件下,并表明,经验特征值分布收敛到Marcenko-Pastur分布。我们的研究结果是潜在的有用,以解决问题的测试是否大量的时间序列是不相关或不。

英文摘要:

We consider linear spectral statistics built from the block-normalized correlation matrix of a set of $M$ mutually independent scalar time series. This matrix is composed of $M \times M$ blocks that contain the sample cross correlation between pairs of time series. In particular, each block has size $L \times L$ and contains the sample cross-correlation measured at $L$ consecutive time lags between each pair of time series. Let $N$ denote the total number of consecutively observed windows that are used to estimate these correlation matrices. We analyze the asymptotic regime where $M,L,N \rightarrow +\infty$ while $ML/N \rightarrow c_\star$, $0<c_\star<\infty$. We study the behavior of linear statistics of the eigenvalues of this block correlation matrix under these asymptotic conditions and show that the empirical eigenvalue distribution converges to a Marcenko-Pastur distribution. Our results are potentially useful in order to address the problem of testing whether a large number of time series are uncorrelated or not.

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