Annals of Statistics
- Ann. Statist.
- Volume 46, Number 4 (2018), 1383-1414.
Large covariance estimation through elliptical factor models
We propose a general Principal Orthogonal complEment Thresholding (POET) framework for large-scale covariance matrix estimation based on the approximate factor model. A set of high-level sufficient conditions for the procedure to achieve optimal rates of convergence under different matrix norms is established to better understand how POET works. Such a framework allows us to recover existing results for sub-Gaussian data in a more transparent way that only depends on the concentration properties of the sample covariance matrix. As a new theoretical contribution, for the first time, such a framework allows us to exploit conditional sparsity covariance structure for the heavy-tailed data. In particular, for the elliptical distribution, we propose a robust estimator based on the marginal and spatial Kendall’s tau to satisfy these conditions. In addition, we study conditional graphical model under the same framework. The technical tools developed in this paper are of general interest to high-dimensional principal component analysis. Thorough numerical results are also provided to back up the developed theory.
Ann. Statist., Volume 46, Number 4 (2018), 1383-1414.
Received: July 2015
Revised: January 2017
First available in Project Euclid: 27 June 2018
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62H25: Factor analysis and principal components; correspondence analysis
Secondary: 62H12: Estimation
Fan, Jianqing; Liu, Han; Wang, Weichen. Large covariance estimation through elliptical factor models. Ann. Statist. 46 (2018), no. 4, 1383--1414. doi:10.1214/17-AOS1588. https://projecteuclid.org/euclid.aos/1530086420
- Technical proofs. This supplementary material contains all the remaining proofs and technical lemmas and the comparison of relative error norms.