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Relative Performance of Expected and Observed Fisher Information in Covariance Estimation for Maximum Likelihood Estimates
Relative Performance Expected and Observed Fisher Information Covariance Estimation Maximum Likelihood Estimates
2013/6/13
Maximum likelihood estimation is a popular method in statistical inference. As a way of assessing the accuracy of the maximum likelihood estimate (MLE), the calculation of the covariance matrix of the...
Covariance Estimation for Distributions with 2+εMoments
Covariance Estimation Distributions 2+εMoments
2011/7/7
We study the minimal sample size N=N(n) that suffices to estimate the covariance matrix of an n-dimensional distribution by the sample covariance matrix in the operator norm, and with an arbitrary fix...
Sparse Inverse Covariance Estimation via an Adaptive Gradient-Based Method
Sparse Covariance Estimation Adaptive Gradient-Based Method
2011/7/6
We study the problem of estimating from data, a sparse approximation to the inverse covariance matrix. Estimating a sparsity constrained inverse covariance matrix is a key component in Gaussian graphi...
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 ...
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...
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 ...
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...
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...