Cholesky-based model averaging for covariancematrix estimation  

作　　者：Hao Zheng Kam-Wah Tsui Xiaoning Kang Xinwei Deng 

机构地区：[1]Gilead Sciences,Inc.,Foster City,CA,USA [2]Department of Statistics,University ofWisconsin-Madison,Madison,WI,USA [3]International Business College,Dongbei University of Finance and Economics,Dalian,China [4]Department of Statistics,Virginia Tech,Blacksburg,VA,USA

出　　处：《Statistical Theory and Related Fields》2017年第1期48-58,共11页统计理论及其应用（英文）

基　　金：National Science of Foundation of China[grant number NSFC-71531004];NNSF.

摘　　要：Estimation of large covariance matrices is of great importance in multivariate analysis.The modified Cholesky decomposition is a commonly used technique in covariance matrix estimation given a specific order of variables.However,information on the order of variables is often unknown,or cannot be reasonably assumed in practice.In this work,we propose a Choleskybased model averaging approach of covariance matrix estimation for high dimensional datawith proper regularisation imposed on the Cholesky factor matrix.The proposed method not only guarantees the positive definiteness of the covariance matrix estimate,but also is applicable in general situations without the order of variables being pre-specified.Numerical simulations are conducted to evaluate the performance of the proposed method in comparison with several other covariance matrix estimates.The advantage of our proposed method is further illustrated by a real case study of equity portfolio allocation.

关 键 词：High-dimension ensemble estimate Cholesky factor positive definite portfolio strategy 

分 类 号：O17[理学—数学]