High Dimensional Covariance Matrix Estimation
Band Width Selection for High Dimensional Covariance Matrix Estimation
... The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti- mators. Both estimators require a band width ...
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Group Lasso Estimation of High-dimensional Covariance Matrices
... a high-dimensional setting by using the assumption that the process X has a sparse representation in a large dictionary of basis ...a matrix regression model as in Bigot et ...for ...
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High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
... variance matrix, which implies that the variables under consideration approximately satisfy marginal independence, corresponding to the zero pattern of the covariance matrix (Kauer- mann, 1996) (and ...
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Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation
... Table 3 reports the average empirical bias and standard deviation of the five bandwidth estimators based on 100 replications. We observe from Table 3 that the overall performance of the proposed estimators is better than ...
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High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation
... graph estimation algorithm by deriving new results about the theory of linear ...inverse covariance matrix of the joint distribution is identical to the edge structure of the moralized ...inverse ...
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Estimation of covariance, correlation and precision matrices for high dimensional data
... multi-dimensional high-frequency data [A¨ıt-Sahalia et ...of high frequency sampling in the empirical literature range from 5-min intervals [Barndorff-Nielsen and Shephard, 2002] to as long as 30-min ...
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Two Sample Tests for High Dimensional Covariance Matrices
... ance matrix based on the random matrix theory are developed in Bai (1993), Bai and Yin (1993), Tracy and Widom (1996), Johnstone (2001) and El Karoui (2007), Johnstone and Lu (2009), Bai and Silverstein ...
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Dissecting High-Dimensional Phenotypes with Bayesian Sparse Factor Analysis of Genetic Covariance Matrices
... the estimation of the magnitude of each latent ...loadings matrix L (see section Priors), we order the latent traits (columns of L) in terms of decreasing in- fluence on the total phenotypic variation and ...
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High-dimensional Covariance Estimation Based On Gaussian Graphical Models
... for covariance and inverse covariance estimation based on a directed acyclic ...since estimation of an equivalence class of directed acyclic graphs is difficult and ...on estimation of ...
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Eigenvalue regularized covariance matrix estimators for high dimensional data
... a covariance matrix or its inverse, called the precision matrix, is an important and sometimes inevitable task in data ...risk estimation and portfolio allocation in finance to classification ...
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High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
... High dimensional (inverse) covariance matrix estimation is becoming more and more common in various scientific and technological ...the covariance matrix, and based on ...
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Array Aperture Extension Algorithm for 2-D DOA Estimation with L-Shaped Array
... of high-resolution algorithms were proposed in literatures [2–6] in the past ...the covariance matrix of the received signals to estimate the DOAs of the signal ...the covariance matrix ...
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Testing Independence for a Large Number of High–Dimensional Random Vectors
... sample covariance matrix S under the finite second moment condition on the ...sample covariance matrices whose columns are covariance stationary time series under the finite fourth moment ...
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Automatic positive semidefinate HAC covariance matrix and GMM estimation
... ! covariance matrix estimators + The standard HAC estimation method reweights estimators of the autocovariances + Here we initially smooth the data observations themselves using kernel function–based ...
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Support estimation of a sample space-time covariance matrix
... ensemble estimation error in (6) represents the expected mean square value of the error E(z) •—◦ E[τ] in (4), which is directly linked to the perturbation of the eigenvalues and eigenvectors of the parahermitian ...
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Multichannel adaptive signal detection in space-time colored compound-gaussian autoregressive processes
... sample covariance matrix (SCM) even in compound-Gaussian ...sample matrix is the maximum likeli- hood estimate (MLE) of the covariance matrix for Gaus- sian disturbance, but is no ...
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Impact of space-time covariance estimation errors on a parahermitian matrix EVD
... polynomial matrix algebra can extend the relative ease of formulating nar- rowband problems to the broadband case [12], ...sequential matrix diagonalisation (SMD) algorithms [4], [18] have been designed to ...
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Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
... Primary motivation for proposing the model in (1) and developing corresponding esti- mation and inferential procedures comes from an application in neuroscience. Graphical models are widely used to estimate and explore ...
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The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R
... We develop an R package fastclime for solving a family of regularized linear program- ming (LP) problems. Our package efficiently implements the parametric simplex algorithm, which provides a scalable and sophisticated ...
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Exploration of the Foreign Exchange Forward Premiums and the Spot Exchange Return: A Multivariate Approach
... of high correlations between the unconditional EUR/USD forward exchange premiums at different horizons and the possible effect of asymmetric shocks on the conditional ...The estimation results show that the ...
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