Annals of Statistics

Large covariance estimation through elliptical factor models

Jianqing Fan, Han Liu, and Weichen Wang

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Abstract

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.

Article information

Source
Ann. Statist., Volume 46, Number 4 (2018), 1383-1414.

Dates
Received: July 2015
Revised: January 2017
First available in Project Euclid: 27 June 2018

Permanent link to this document
https://projecteuclid.org/euclid.aos/1530086420

Digital Object Identifier
doi:10.1214/17-AOS1588

Mathematical Reviews number (MathSciNet)
MR3819104

Zentralblatt MATH identifier
06936465

Subjects
Primary: 62H25: Factor analysis and principal components; correspondence analysis
Secondary: 62H12: Estimation

Keywords
Principal component analysis approximate factor model sub-Gaussian family elliptical distribution conditional graphical model marginal and spatial Kendall’s tau

Citation

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


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    Zentralblatt MATH: 1360.62394
    Digital Object Identifier: doi:10.1214/07-AOS507
    Project Euclid: euclid.aos/1211819558

Supplemental materials

  • Technical proofs. This supplementary material contains all the remaining proofs and technical lemmas and the comparison of relative error norms.
    Digital Object Identifier: doi:10.1214/17-AOS1588SUPP
    Supplemental files are immediately available to subscribers. Non-subscribers gain access to supplemental files with the purchase of the article.

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