搜索结果: 31-45 共查到“统计学 Covariance”相关记录63条 . 查询时间(0.122 秒)
Some covariance models based on normal scale mixtures
cross covariance function Gneiting's class rainfall model spatio-temporal model
2011/3/24
Modelling spatio-temporal processes has become an important issue in current research. Since Gaussian processes are essentially determined by their second order structure, broad classes of covariance ...
Limiting Laws of Coherence of Random Matrices with Applications to Testing Covariance Structure and Construction of Compressed Sensing Matrices
Chen-Stein method coherence compressed sensing matrix covariance struc-ture law of large numbers limiting distribution maxima moderate deviations mutual incoherence property random matrix sample correlation matrix
2011/3/23
Testing covariance structure is of significant interest in many areas of statistical analysis and construction of compressed sensing matrices is an important problem in signal processing. Motivated b...
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...
Sparse Inverse Covariance Estimation via the Split Bregman Method
Machine Learning (stat.ML) Learning (cs.LG)
2010/12/17
We consider the problem of learning the structure of graphical models by estimating the inverse covariance matrix with sparsity regularization. We develop a new method based on split Bregman to solve ...
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...
First of all we want to thank the editor, Michael Newton, for leading the review and discussion of our work.
A Random Matrix--Theoretic Approach to Handling Singular Covariance Estimates
Random Matrix--Theoretic Approach Handling Singular Covariance Estimates
2010/10/19
In many practical situations we would like to estimate the covariance matrix of a set of variables from an insufficient amount of data. More specifically, if we have a set of $N$ independent, identica...
Distance correlation is a new class of multivariate dependence coefficients applicable to random vectors of arbitrary and not necessarily equal dimension. Distance covariance and distance correlation...
On the covariance of the asymptotic empirical copula process
Asymptotic variance copula dependence parameter empirical process independence left-tail decreasing rank-based inference
2010/3/18
Conditions are given under which the empirical copula process associated with
a random sample from a bivariate continuous distribution has a smaller asymptotic
covariance function than the standard ...
Sparse covariance estimation in heterogeneous samples
Covariance selection Dirichlet process Gaussian graphical model HiddenMarkov model Nonparametric Bayes inference
2010/3/9
Standard Gaussian graphical models (GGMs) implicitly assume that the conditional independence
among variables is common to all observations in the sample. However, in practice,
observations are usua...
A Multivariate Variance Components Model for Analysis of Covariance in Designed Experiments
Adjusted mean blocking factor conditionalmodel orthogonal design randomized blocks design.itute of Mathematical Statistics
2010/3/9
Traditional methods for covariate adjustment of treatment
means in designed experiments are inherently conditional on the ob-
served covariate values. In order to develop a coherent general method-
...
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...
A Comparison of Analysis of Covariate-Adjusted Residuals and Analysis of Covariance
allometry ANOVA clustering homogeneity of variances isometry Kruskal-Wallis test linearmodels parallel lines model
2010/3/19
Various methods to control the influence of a covariate on a response variable are compared. In particular,ANOVA with or without homogeneity of variances (HOV) of errors and Kruskal-Wallis (K-W) tests...