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Band Width Selection for High Dimensional Covariance Matrix Estimation
Bandable covariance Banding estimator Large p, small n Ratio- consistency Tapering estimator Thresholding estimator
2016/1/25
The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti-mators. Both estimators require choosing a ban...
Band Width Selection for High Dimensional Covariance Matrix Estimation
Bandable covariance Banding estimator Large p small n
2016/1/20
The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti-mators. Both estimators require choosing a ban...
Covariance Matrix Estimation for Stationary Time Series
Autocovariance matrix banding large deviation physical dependence mea-sure short range dependence spectral density stationary process tapering thresholding Toeplitz matrix
2011/6/20
We obtain a sharp convergence rate for banded covariance matrix estimates of stationary
processes. A precise order of magnitude is derived for spectral radius of sample covariance matrices.
We also ...
High Dimensional Covariance Matrix Estimation in Approximate Factor Models
sparse estimation thresholding cross-sectional correlation common factors idiosyncratic seemingly unrelated regression
2011/6/20
The variance covariance matrix plays a central role in the inferential theories
of high dimensional factor models in finance and economics. Popular
regularization methods of directly exploiting spar...
Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models
Covariance selection Reduced conditional sampling Variable selection
2010/4/29
Estimating a covariance matrix efficiently and discovering its structure are important
statistical problems with applications in many fields. This article takes a Bayesian
approach to estimate the c...
High Dimensional Covariance Matrix Estimation Using a Factor Model
Factor model diverging dimensionality covariance matrixestimation consistency asymptotic normality optimal portfolio
2010/4/26
High dimensionality comparable to sample size is common in many statistical
problems. We examine covariance matrix estimation in the asymptotic
framework that the dimensionality p tends to ∞ as the ...