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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
2016/1/26
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
2016/1/25
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...
Optimal reinsurance minimizing the distortion risk measure under general reinsurance premium principles Matrices
Optimal reinsurance Distortion risk measure Reinsurance pre- mium principle Wang’s premium principle VaR TVaR
2016/1/25
Recently the optimal reinsurance strategy concerning the insurer’s risk attitude and the reinsurance premium principle is an interesting topic. This paper discusses the optimal reinsurance problem wit...
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...
Optimal reinsurance minimizing the distortion risk measure under general reinsurance premium principles Matrices
Optimal reinsurance Distortion risk measure Wang’s premium principle VaR TVaR
2016/1/20
Recently the optimal reinsurance strategy concerning the insurer’s risk attitude and the reinsurance premium principle is an interesting topic. This paper discusses the optimal reinsurance problem wit...
QML estimation of spatial dynamic panel data models with time varying spatial weights matrices
Spatial autoregression Dynamic panels Time varying spatial weights matrix Fixed ef- fects Maximum likelihood
2016/1/19
This paper investigates the quasi-maximum likelihood estimation of spatial dynamic panel data mod-els where spatial weights matrices can be time varying. We …nd that QML estimate is consistent and asy...
24th International Workshop on Matrices and Statistics
International Workshop Matrices Statistics
2014/11/5
On behalf of the International Organizing Committee and the Local Organizing Committee
we have much pleasure in extending a very cordial invitation to participate in this 24th
International Worksh...
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...
Regression with Distance Matrices
functional data analysis mixed data multidimensional scaling shape correlation ma-trix
2013/4/27
Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these...
Distribution of the largest eigenvalue for real Wishart and Gaussian random matrices and a simple approximation for the Tracy-Widom distribution
Random Matrix Theory characteristic roots largest eigenvalue Tracy-Widom Distribution Wishart Matrices Gaussian Orthogonal Ensemble
2012/11/23
We derive the exact distribution of the largest eigenvalue for finite dimensions real Wishart matrices and for the Gaussian Orthogonal Ensemble (GOE). We compare the exact distribution with the Tracy-...
General lower bounds on maximal determinants of binary matrices
General lower bounds maximal determinants binary matrices
2012/9/18
We give general lower bounds on the maximal determinant ofn×n{+1,−1}matrices, both with and without the assumption of the Hadamard conjecture. Our bounds improve on earlier resultsof de Launey a...
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...