ArticleAnnals of statistics2021
A SHRINKAGE PRINCIPLE FOR HEAVY-TAILED DATA: HIGH-DIMENSIONAL ROBUST LOW-RANK MATRIX RECOVERY.
Article in Annals of statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Understanding Implicit Regularization in Over-Parameterized Single Index Model.Journal of the American Statistical Association · 2023Article
- Adaptive Huber Regression on Markov-dependent Data.Stochastic processes and their applications · 2022Article
- Supervised dimensionality reduction for big data.Nature communications · 2021Article
- Robust high dimensional factor models with applications to statistical machine learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Adaptive Huber Regression.Journal of the American Statistical Association · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper introduces a simple principle for robust statistical inference via appropriate shrinkage on the data. This widens the scope of high-dimensional techniques, reducing the distributional conditions from sub-exponential or sub-Gaussian to more relaxed bounded second or fourth moment. As an illustration of this principle, we focus on robust estimation of the low-rank matrix
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.