Bernoulli

Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution

Fang Han and Han Liu

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Abstract

Correlation matrices play a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson’s sample correlation matrix. Although Pearson’s sample correlation matrix enjoys various good properties under Gaussian models, it is not an effective estimator when facing heavy-tailed distributions. As a robust alternative, Han and Liu [J. Am. Stat. Assoc. 109 (2015) 275–287] advocated the use of a transformed version of the Kendall’s tau sample correlation matrix in estimating high dimensional latent generalized correlation matrix under the transelliptical distribution family (or elliptical copula). The transelliptical family assumes that after unspecified marginal monotone transformations, the data follow an elliptical distribution. In this paper, we study the theoretical properties of the Kendall’s tau sample correlation matrix and its transformed version proposed in Han and Liu [J. Am. Stat. Assoc. 109 (2015) 275–287] for estimating the population Kendall’s tau correlation matrix and the latent Pearson’s correlation matrix under both spectral and restricted spectral norms. With regard to the spectral norm, we highlight the role of “effective rank” in quantifying the rate of convergence. With regard to the restricted spectral norm, we for the first time present a “sign sub-Gaussian condition” which is sufficient to guarantee that the rank-based correlation matrix estimator attains the fast rate of convergence. In both cases, we do not need any moment condition.

Article information

Source
Bernoulli, Volume 23, Number 1 (2017), 23-57.

Dates
Received: November 2013
Revised: November 2014
First available in Project Euclid: 27 September 2016

Permanent link to this document
https://projecteuclid.org/euclid.bj/1475001347

Digital Object Identifier
doi:10.3150/15-BEJ702

Mathematical Reviews number (MathSciNet)
MR3556765

Zentralblatt MATH identifier
1359.62186

Keywords
double asymptotics elliptical copula Kendall’s tau correlation matrix rate of convergence transelliptical model

Citation

Han, Fang; Liu, Han. Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution. Bernoulli 23 (2017), no. 1, 23--57. doi:10.3150/15-BEJ702. https://projecteuclid.org/euclid.bj/1475001347


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