ArticleJournal of econometrics2019
Robust Covariance Estimation for Approximate Factor Models.
Article in Journal of econometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Information criteria for latent factor models: a study on factor pervasiveness and adaptivity.Journal of econometrics · 2023Article
- Understanding Implicit Regularization in Over-Parameterized Single Index Model.Journal of the American Statistical Association · 2023Article
- Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- BRIDGING CONVEX AND NONCONVEX OPTIMIZATION IN ROBUST PCA: NOISE, OUTLIERS, AND MISSING DATA.Annals of statistics · 2021Article
- Simultaneous Inference for High-Dimensional Approximate Factor Model.Entropy (Basel, Switzerland) · 2020Article
- NOISY MATRIX COMPLETION: UNDERSTANDING STATISTICAL GUARANTEES FOR CONVEX RELAXATION VIA NONCONVEX OPTIMIZATION.SIAM journal on optimization : a publication of the Society for Industrial and Applied Mathematics · 2020Article
- Article
- AnJournal of machine learning research : JMLR · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this paper, we study robust covariance estimation under the approximate factor model with observed factors. We propose a novel framework to first estimate the initial joint covariance matrix of the observed data and the factors, and then use it to recover the covariance matrix of the observed data. We prove that once the initial matrix estimator is good enough to maintain the element-wise optimal rate, the whole procedure will generate an estimated covariance with desired properties. For data with only bounded fourth moment, we propose to use adaptive Huber loss minimization to give the initial joint covariance estimation. This approach is applicable to a much wider class of distributions, beyond sub-Gaussian and elliptical distributions. We also present an asymptotic result for adaptive Huber's M-estimator with a diverging parameter. The conclusions are demonstrated by extensive simulations and real data analysis.
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