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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...
Range Image Sequence Analysis by 2.5-D Least-Squares-Tracking with Variance Component Estimation and Robust Variance Covariance Matrix Estimation
Range Imaging Least Squares Tracking Variance Component Estimation
2015/12/17
In this article, a range image sequence tracking approach is proposed, which combines 3-D camera intensity and range observations
in an integrated geometric transformation model. Based on 2-D least s...
Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
Time Series Covariance Estimation Regularization, Sparsity
2011/7/6
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a...
Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
Time Series Covariance Estimation Regularization Sparsity Thresholding Semiparametrics Graphical Model Variable Clustering
2011/7/5
To better understand the spatial structure of large panels of economic and nancial time
series and provide a guideline for constructing semiparametric models, this paper rst consid-
ers estimating...
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...
Adaptive Thresholding for Sparse Covariance Matrix Estimation
constrained ℓ 1 minimization covariance matrix Frobenius norm Gaus-sian graphical model rate of convergence precision matrix spectral norm
2011/3/21
In this paper we consider estimation of sparse covariance matrices and propose a thresholding procedure which is adaptive to the variability of individual entries. The estimators are fully data driven...
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 ...