Covariance Regression Analysis

• 成果类型: Article
• 署名作者: Zou, Tao; Lan, Wei; Wang, Hansheng; Tsai, Chih-Ling
• 署名单位: Peking University; Australian National University; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; University of California System; University of California Davis
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1131699
• 发表日期: 2017
• 页码: 266-281
• 关键词: PORTFOLIO OPTIMIZATION; Matrix Estimation; RISK; selection; CHOICE

摘要

This article introduces covariance regression analysis for a p-dimensional response vector. The proposed method explores the regression relationship between the p-dimensional covariance matrix and auxiliary information. We study three types of estimators: maximum likelihood, ordinary least squares, and feasible generalized least squares estimators. Then, we demonstrate that these regression estimators are consistent and asymptotically normal. Furthermore, we obtain the high dimensional and large sample properties of the corresponding covariance matrix estimators. Simulation experiments are presented to demonstrate the performance of both regression and covariance matrix estimates. An example is analyzed from the Chinese stock market to illustrate the usefulness of the proposed covariance regression model. Supplementary materials for this article are available online.