搜索结果: 46-60 共查到“统计学 Covariance”相关记录63条 . 查询时间(0.161 秒)
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 ...
Missing values and sparse inverse covariance estimation
Missing values sparse inverse covariance estimation
2010/3/19
We propose an `1-regularized likelihood method for estimating the inverse
covariance matrix in the high-dimensional multivariate normal model
in presence of missing data. Our method is based on the ...
Wigner theorems for random matrices with dependent entries：Ensembles associated to symmetric spaces and sample covariance matrices
Wigner theorem symmetric space sample covariance
2009/3/20
It is a classical result of Wigner that for an hermitian matrix with independent entries on and above the diagonal, the mean empirical eigenvalue distribution converges weakly to the semicircle law as...
Optimising prediction error among completely monotone covariance sequences
Gaussian time series prediction error covariance sequences
2009/3/19
We provide a characterisation of Gaussian time series which optimise the one-step prediction error subject to the covariance sequence being completely monotone with the first m covariances specified. ...
Some Tests Concerning the Covariance Matrix in High Dimensional Data
asymptotic distributions multivariate normal null and non-null distributions sample size smaller than the dimension
2009/3/9
In this paper, tests are developed for testing certain hypotheses on the covariance matrix Σ, when the sample size N = n + 1 is smaller than the dimension pof the data. Under the condition that (tr Σi...
Covariance estimation in decomposable Gaussian graphical models
Covariance estimation decomposable Gaussian graphical models
2010/3/18
Graphical models are a framework for representing and exploiting prior conditional independence structures within distributions using graphs. In the Gaussian case, these models are directly related to...
Nonparametric Test for Eigenvalues of Covariance Matrix in Multipopulation
eigenvalues k-sample Mood test nonparametric test principal component score
2009/3/5
We propose a nonparametric procedure to test the hypothesis that the j-th largest eigenvalues of a covariance matrix are equal in multipopulation. We apply the Mood test by using the principal compone...
A new approach to Cholesky-based covariance regularization in high dimensions
new approach Cholesky-based covariance regularization high dimensions
2010/3/18
In this paper we propose a new regression interpretation of the Cholesky factor of the covariance matrix, as opposed to the well known regression interpretation of the Cholesky factor of the inverse c...
On best affine unbiased covariance-preserving prediction of factor scores
Factor analysis factor scores covariance-preserving Kristof-type theorem
2009/2/23
This paper gives a generalization of results presented by ten Berge, Krijnen, Wansbeek & Shapiro.They examined procedures and results as proposed by Anderson & Rubin, McDonald, Green and Krijnen, Wans...
Bayesian joint modelling of the mean and covariance structures for normal longitudinal data
Antedependence models Bayes estimation Fisher scoring Gibbs sampling
2009/2/23
We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters and the innovation variances in a longitudinal data context. We ...
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...
Nonstationary covariance models for global data
Nonstationary covariance function processes on spheres TOMS ozone data fast Fourier transform
2010/3/17
With the widespread availability of satellite-based instruments,
many geophysical processes are measured on a global scale and they
often show strong nonstationarity in the covariance structure. In ...
Operator norm consistent estimation of large-dimensional sparse covariance matrices
Covariance matrices correlation matrices adjacency matrices eigenvalues of covariance matrices multivariate statistical analysis
2010/3/17
Estimating covariance matrices is a problem of fundamental importance
in multivariate statistics. In practice it is increasingly frequent
to work with data matrices X of dimension n×p, where p and n...
Flexible covariance estimation in graphical Gaussian models
Covariance estimation Gaussian graphical models Bayes estimators shrinkage regularization
2010/3/17
In this paper, we propose a class of Bayes estimators for the
covariance matrix of graphical Gaussian models Markov with respect
to a decomposable graph G. Working with the WPG family defined
by Le...
Covariance regularization by thresholding
Covariance estimation regularization sparsity thresholding large p smalln high dimension low sample size
2010/3/17
This paper considers regularizing a covariance matrix of p variables
estimated from n observations, by hard thresholding. We show
that the thresholded estimate is consistent in the operator norm as
...