Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution

Fang Han, Han Liu

Research output: Contribution to journalReview article

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.

Original languageEnglish (US)
Pages (from-to)23-57
Number of pages35
JournalBernoulli
Volume23
Issue number1
DOIs
StatePublished - Feb 1 2017

Keywords

  • Double asymptotics
  • Elliptical copula
  • Kendall's tau correlation matrix
  • Rate of convergence
  • Transelliptical model

ASJC Scopus subject areas

  • Statistics and Probability

Fingerprint Dive into the research topics of 'Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution'. Together they form a unique fingerprint.

  • Cite this