Non-linear shrinkage estimation of large-scale structure covariance
Non-linear shrinkage estimation of large-scale structure covariance
复制标题
大尺度结构协方差的非线性收缩估计
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
B. Joachimi
中科院分区：
文献类型：
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作者：
B. Joachimi
在许多天体物理学环境中，大数据集的协方差矩阵必须根据有限数量的模拟实现来经验地确定。由此产生的噪声降低了推理，如果实现比数据点少，则完全排除推理。这项工作适用于最近提出的非线性收缩估计的协方差从大尺度结构宇宙学的一个现实的例子。在优化其性能用于似然表达式后，收缩估计量产生的次显性偏差和方差与标准估计量相当，其实现因子为1050。这是在没有任何关于数据属性或协方差矩阵结构的先验信息的情况下以可忽略的计算成本实现的。
In many astrophysical settings, covariance matrices of large data sets have to be determined empirically from a finite number of mock realizations. The resulting noise degrades inference and precludes it completely if there are fewer realizations than data points. This work applies a recently proposed non-linear shrinkage estimator of covariance to a realistic example from large-scale structure cosmology. After optimizing its performance for the usage in likelihood expressions, the shrinkage estimator yields subdominant bias and variance comparable to that of the standard estimator with a factor of ∼50 less realizations. This is achieved without any prior information on the properties of the data or the structure of the covariance matrix, at a negligible computational cost.
影响因子：
6.5
作者：
Hartlap, J.;Simon, P.;Schneider, P.
通讯作者：
Schneider, P.