Fan, Jianqing and Liao, Yuan and Mincheva, Martina (2011): Large covariance estimation by thresholding principal orthogonal complements.

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Abstract
This paper deals with estimation of highdimensional covariance with a conditional sparsity structure, which is the composition of a lowrank matrix plus a sparse matrix. By assuming sparse error covariance matrix in a multifactor model, we allow the presence of the crosssectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure. The POET estimator includes the sample covariance matrix, the factorbased covariance matrix (Fan, Fan and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specic examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms, including the spectral norm. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also veried by extensive simulation studies.
Item Type:  MPRA Paper 

Original Title:  Large covariance estimation by thresholding principal orthogonal complements 
Language:  English 
Keywords:  High dimensionality, approximate factor model, unknown factors, principal components, sparse matrix, lowrank matrix, thresholding, crosssectional correlation 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  38697 
Depositing User:  Yuan Liao 
Date Deposited:  10 May 2012 01:41 
Last Modified:  26 Sep 2019 16:34 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/38697 