搜索结果: 1-12 共查到“理论统计学 Covariance Matrix”相关记录12条 . 查询时间(0.125 秒)
Testing the Diagonality of a Large Covariance Matrix in a Regression Setting
Bias-Corrected Test Covariance Diagonality Test High Di- mensional Data
2016/1/26
In multivariate analysis, the covariance matrix associated with a set of vari-ables of interest (namely response variables) commonly contains valuable infor-mation about the dataset. When the dimensio...
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...
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Parallel Gaussian Process Regression Low-Rank Covariance Matrix Approximations
2013/6/14
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due ...
Stable Estimation of a Covariance Matrix Guided by Nuclear Norm Penalties
Covariance estimation Regularization Condition number Canonical correlation analysis Discriminant analysis Clustering
2013/6/14
Estimation of covariance matrices or their inverses plays a central role in many statistical methods. For these methods to work reliably, estimated matrices must not only be invertible but also well-c...
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...
A Weak Law of Large Numbers for the Sample Covariance Matrix
Law of large numbers,affine normalization sample covariance domain of attraction generalized domain of attraction
2009/5/4
In this article we consider the sample covariance matrix formed from a sequence of independent and identically distributed random vectors from the generalized domain of attraction of the multivariate ...
Corrections to LRT on Large Dimensional Covariance Matrix by RMT
High-dimensional data Testing on covariance matrices Marcenko-Pastrur distributions Random F-matrices
2010/3/18
In this paper, we give an explanation to the failure of two likelihood ratio procedures
for testing about covariance matrices from Gaussian populations when the dimension
is large compared to the sa...
Universality results for largest eigenvalues of some sample covariance matrix ensembles
Universality results largest eigenvalues sample covariance matrix ensembles
2010/4/29
For sample covariance matrices with iid entries with sub-Gaussian
tails, when both the number of samples and the number of variables
become large and the ratio approaches to one, it is a well-known ...
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 ...
