ECA: High-Dimensional Elliptical Component Analysis in Non-Gaussian Distributions

• 成果类型: Article
• 署名作者: Han, Fang; Liu, Han
• 署名单位: University of Washington; University of Washington Seattle; Princeton University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1246366
• 发表日期: 2018
• 页码: 252-268
• 关键词: covariance-matrix estimation; sparse principal components; rank-based inference; statistical-analysis; optimal tests; power method; r-estimation; sign; interdirections; DECOMPOSITION

摘要

We present a robust alternative to principal component analysis (PCA)called elliptical component analysis (ECA)for analyzing high-dimensional, elliptically distributed data. ECA estimates the eigenspace of the covariance matrix of the elliptical data. To cope with heavy-tailed elliptical distributions, a multivariate rank statistic is exploited. At the model-level, we consider two settings: either that the leading eigenvectors of the covariance matrix are nonsparse or that they are sparse. Methodologically, we propose ECA procedures for both nonsparse and sparse settings. Theoretically, we provide both nonasymptotic and asymptotic analyses quantifying the theoretical performances of ECA. In the nonsparse setting, we show that ECA's performance is highly related to the effective rank of the covariance matrix. In the sparse setting, the results are twofold: (i) we show that the sparse ECA estimator based on a combinatoric program attains the optimal rate of convergence; (ii) based on some recent developments in estimating sparse leading eigenvectors, we show that a computationally efficient sparse ECA estimator attains the optimal rate of convergence under a suboptimal scaling. Supplementary materials for this article are available online.