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A far-near sparse covariance model with application in climatology
Environmental and Ecological Statistics ( IF 3.8 ) Pub Date : 2020-09-18 , DOI: 10.1007/s10651-020-00462-4
Yi Li , Aidong Adam Ding

Teleconnection, the strong dependence between two distant locations, provides interesting information for discovering the structures in spatial data. While teleconnections are often sparse and estimated through sample correlations, there are also abundant correlations among nearby locations. We propose a far-near covariance model that simultaneously models the abundant short-distance dependencies and the sparse long-distance dependence. This approach provides a new framework for utilizing the short-distance dependence structure to improve the exploration and estimation of the sparse remote dependence signals. The statistical properties of proposed estimators are provided. The detection of teleconnection in high-dimensional data is a multiple testing problem. We relate this detection problem to \(\tau \)-coherence statistical testing and generalize the \(\tau \)-coherence for the covariance matrix of two-dimensional grid locations. The applications are illustrated through numerical studies on both synthetic data and real climate data.



中文翻译:

近距离稀疏协方差模型在气候学中的应用

遥距连接是两个遥远位置之间的强烈依存关系,它为发现空间数据中的结构提供了有趣的信息。虽然远程联系通常是稀疏的,并通过样本相关性进行估算,但是附近位置之间也存在大量相关性。我们提出了一个近距离协方差模型,该模型同时对大量的短距离依赖关系和稀疏的长距离依赖关系进行建模。该方法为利用短距离相关性结构改善稀疏远程相关性信号的探索和估计提供了新的框架。提供了拟议估计量的统计性质。在高维数据中检测远程连接是一个多重测试问题。我们将此检测问题与\(\ tau \)-相干统计测试并推广二维网格位置的协方差矩阵的\(\ tau \)-相干性。通过对合成数据和实际气候数据的数值研究说明了这些应用。

更新日期:2020-09-20
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