摘要翻译：
我们证明了当高维数据矩阵是低秩矩阵与独立项随机误差矩阵之和时，通过求解凸极小问题可以一致估计低秩分量。我们发展了一个新的理论论证，在不假设稀疏性或误差矩阵的任何矩存在的情况下建立一致性，从而允许像Cauchy这样的胖尾连续随机误差。仿真结果表明了这一结论。
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英文标题：
《Robust Principal Component Analysis with Non-Sparse Errors》
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作者：
Jushan Bai and Junlong Feng
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  We show that when a high-dimensional data matrix is the sum of a low-rank matrix and a random error matrix with independent entries, the low-rank component can be consistently estimated by solving a convex minimization problem. We develop a new theoretical argument to establish consistency without assuming sparsity or the existence of any moments of the error matrix, so that fat-tailed continuous random errors such as Cauchy are allowed. The results are illustrated by simulations.
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PDF链接：
https://arxiv.org/pdf/1902.08735