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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...
Estimation of Spatial Panel Data Models with Time Varying Spatial Weights Matrices
Spatial autoregression Panel data Time varying spatial weights matrices Fixed e¤ects Maximum likelihood Impact analysis
2016/1/20
This paper investigates the quasi-maximum likelihood (QML) estimation of spatial panel data models where spatial weights matrices can be time varying. We show that QML estimate is consistent and asymp...
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...
Regularity Properties of High-dimensional Covariate Matrices
high-dimensional regression instrumental variables sparse estimation compressed sensing random matrix re-stricted eigenvalue compatibility,ℓ q sensitivity computational complex-ity NP-hardness
2013/6/14
Regularity properties such as the incoherence condition, the restricted isometry property, compatibility, restricted eigenvalue and $\ell_q$ sensitivity of covariate matrices play a pivotal role in hi...
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...
A CLT for Information-theoretic statistics of Non-centered Gram random matrices
Random Matrix Spectral measure Stieltjes Transform
2011/7/19
In this article, we study the fluctuations of the random variable: $$ {\mathcal I}_n(\rho) = \frac 1N \log\det(\Sigma_n \Sigma_n^* + \rho I_N),\quad (\rho>0) $$ where $\Sigma_n= n^{-1/2} D_n^{1/2} X_n...
The LASSO for generic design matrices as a function of the relaxation parameter
linear regression LASSO relaxation parameter
2011/6/16
The LASSO is a variable subset selection procedure in statistical
linear regression based on ℓ1 penalization of the least-squares
operator. Its behavior crucially depends, both in practice and...
Supersymmetry approach to Wishart correlation matrices: Exact results
Statistics Theory (math.ST) Mathematical Physics (math-ph)
2010/12/17
We calculate the marginal probability density of real and complex Wishart correlation matrices. For deep mathematical reasons, no explicit expression could be obtained for the real case so far. We cir...
A Bayesian Statistical Approach for Inference on Static Origin-Destination Matrices
Applications (stat.AP) Methodology (stat.ME)
2010/12/17
We address the problem of static OD matrix estimation from a formal statistical viewpoint. We adopt a novel Bayesian framework to develop a class of models that explicitly cast trip configurations in ...
Approximating the inverse of banded matrices by banded matrices with applications to probability and statistics
infinite band-dominated matrices Gaussian stochastic processes mixing conditions high dimensional statistical inference
2010/3/10
In the first part of this paper we give an elementary proof of the fact that if an infinite matrix
A, which is invertible as a bounded operator on `2, can be uniformly approximated by banded matrices...
On a parametrization of positive semidefinite matrices with zeros
parametrization positive semidefinite matrices zeros
2010/3/9
We study a class of parametrizations of convex cones of positive
semidefinite matrices with prescribed zeros. Each such cone corresponds to
a graph whose non-edges determine the prescribed zeros. Ea...
We give an account of some results, both old and new, about any
n × n Markov matrix that is embeddable in a one-parameter Markov
semigroup. These include the fact that its eigenvalues must lie in a
...
The spectrum of kernel random matrices
spectrum kernel random matrices high-dimensional statisticalinference
2010/3/9
We place ourselves in the setting of high-dimensional statistical
inference where the number of variables p in a dataset of interest is
of the same order of magnitude as the number of observations n...
Inegalites de trace pour des matrices de Toeplitz et applications a des vraisemblances gaussiennes
trace pour des matrices applications a des vraisemblances gaussiennes
2009/9/23
Inegalites de trace pour des matrices de Toeplitz et applications a des vraisemblances gaussiennes。