搜索结果: 1-8 共查到“统计学其他学科 Covariance”相关记录8条 . 查询时间(0.106 秒)
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...
Refining Genetically Inferred Relationships Using Treelet Covariance Smoothing
covariance estimation cryptic relatedness genome-wide associ-ation heritability kinship.
2012/9/17
Recent technological advances coupled with large sample sets have un-covered many factors underlying the genetic basis of traits and the predis-position to complex disease, but much is left to discove...
A perturbative approach to the reconstruction of the eigenvalue spectrum of a normal covariance matrix from a spherically truncated counterpart
A perturbative approach the reconstruction the eigenvalue spectrum a normal covariance matrix a spherically truncated counterpart
2012/9/18
In this paper we propose a perturbative method for the reconstruction of the covariance matrix of a multinormal distribution, under the assumption that the only available information amounts to the co...
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...
Sparse permutation invariant covariance estimation
Covariance matrix High dimension low sample size large p small n Lasso Sparsity Cholesky decomposition
2009/9/16
The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihood approac...
Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables
EM algorithm High-dimension but low-sample size L1 penalization Microarray gene expression Mixture model Penalized likelihood
2009/9/16
Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the presence of many noise...