arXiv 23 Feb 2019 · Econometrics · 2 citations (OpenAlex)
arXiv:1902.08735 · PDF · DOI · OpenAlex · Extracted main text
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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| 1 | Regularized Quantile Regression with Interactive Fixed Effects | 0.405 | 1 | 1 |