搜索结果: 1-15 共查到“Covariance matrices”相关记录21条 . 查询时间(0.092 秒)
Test for Bandedness of High-Dimensional Covariance Matrices and Bandwidth Estimation
Banded covariance matrix Bandwidth estimation High data dimension Large p small n Nonparametric
2016/1/25
Motivated by the latest effort to employ banded matrices to esti-mate a high-dimensional covariance Σ, we propose a test for Σ being banded with possible diverging bandwidth. The test is adaptive to t...
Test for Bandedness of High-Dimensional Covariance Matrices and Bandwidth Estimation
Banded covariance matrix Bandwidth estimation High data dimension Large p small n Nonparametric
2016/1/20
Motivated by the latest effort to employ banded matrices to esti-mate a high-dimensional covariance Σ, we propose a test for Σ being banded with possible diverging bandwidth. The test is adaptive to t...
Construction of non-diagonal background error covariance matrices for global chemical data assimilation
Chemical data assimilation To optimize the noise Model predictions Chemical state
2014/12/9
Chemical data assimilation attempts to optimally use noisy observations along with imperfect model predictions to produce a better estimate of the chemical state of the atmosphere. It is widely accept...
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices
Covariance matrix group sparsity low-rank matrix minimax rate of convergence sparse principal component analysis principal subspace,rank detection
2013/6/14
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices
Covariance matrix group sparsity low-rank matrix minimax rate of convergence sparse principal component analysis principal subspace,rank detection
2013/6/14
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...
Positive Definite $\ell_1$ Penalized Estimation of Large Covariance Matrices
Alternating direction methods Large covariance matrices Matrix norm Positive-de nite estimation Sparsity Soft-thresholding.
2012/9/18
The thresholding covariance estimator has nice asymptotic properties for estimating sparse large covariance matrices, but it often has negative eigenvalues when used in real data analysis. To simultan...
Test for bandedness of high-dimensional covariance matrices and bandwidth estimation
Banded covariance matrix bandwidth estimation high data dimension largep, small n nonparametric.
2012/9/17
Motivated by the latest effort to employ banded matrices to esti-mate a high-dimensional covariance Σ, we propose a test for Σbeing banded with possible diverging bandwidth. The test is adaptive to th...
Local Marchenko-Pastur Law at the Hard Edge of Sample Covariance Matrices
Random matrices ovariance matrices archenko-Pastur law ensity of states elocalization
2012/6/29
Let $X_N$ be a $N\times N$ matrix whose entries are i.i.d. complex random variables with mean zero and variance $\frac{1}{N}$. We study the asymptotic spectral distribution of the eigenvalues of the c...
Two sample tests for high-dimensional covariance matrices
High-dimensional covariance large p small n likelihood ratio test testing for gene-sets
2012/6/21
We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance--covariance matrices, and the other is on off-diagonal sub-matr...
An Efficient Algorithm for Maximum-Entropy Extension of Block-Circulant Covariance Matrices
Efficient Algorithm Maximum-Entropy Extension Block-Circulant Covariance Matrices Optimization and Control
2011/9/5
Abstract: This paper deals with maximum entropy completion of partially specified block-circulant matrices. Since positive definite symmetric circulants happen to be covariance matrices of stationary ...
Estimation of covariance matrices based on hierarchical inverse-Wishart priors
Bayesian covariance estimation Skrinkage Hierarchical Inverse-Wishart prior
2011/7/5
This paper focuses on Bayesian shrinkage for covariance matrix estimation. We examine posterior properties and frequentist risks of Bayesian estimators based on new hierarchical inverse-Wishart priors...
A Note on the Central Limit Theorem for the Eigenvalue Counting Function of Wigner and Covariance matrices
Central Limit Theorem the Eigenvalue Counting Function
2010/11/23
This note presents some central limit theorems for the eigenvalue counting function of Wigner matrices in the form of suitable translations of results by Gustavsson and O'Rourke on the limiting behavi...
Group Lasso estimation of high-dimensional covariance matrices
Group Lasso ℓ 1 penalty high-dimensional covariance estimation basis expansion
2010/10/19
In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensiona...
Adaptive estimation of covariance matrices via Cholesky decomposition
Covariance matrix banding Cholesky decomposition
2010/10/19
This paper studies the estimation of a large covariance matrix. We introduce a novel procedure called ChoSelect based on the Cholesky factor of the inverse covariance. This method uses a dimension red...
Estimating correlation and covariance matrices by weighting of market similarity
Weighted Correlation Estimation Covariance Estimation Time-dynamic Dependence
2010/10/20
We discuss a weighted estimation of correlation and covariance matrices from historical financial data. To this end, we introduce a weighting scheme that accounts for similarity of previous market co...