[PDF] Top 20 Sample space-time covariance matrix estimation
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Sample space-time covariance matrix estimation
... parahermitian matrix — typically a cross-spectral density (CSD) matrix emerging as the z-transform of a space-time covariance matrix — can be decomposed into a product of ... See full document
5
Multichannel adaptive signal detection in space-time colored compound-gaussian autoregressive processes
... To overcome the above difficulty caused by large joint spatial-temporal dimension, the structural information about the disturbance space-time covariance matrix can be exploited. More ... See full document
18
Group Lasso Estimation of High-dimensional Covariance Matrices
... the covariance matrix Σ by expanding the process X into a dictionary of basis ...suitable matrix regression model. This new approach to covariance estimation is well adapted to the case ... See full document
39
High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
... inverse covariance matrix when maximizing the normal log-likelihood and therefore encourages some of the entries of the estimated inverse covariance matrix to be exact ...the sample ... See full document
26
Market Risk Beta Estimation using Adaptive Kalman Filter
... beta estimation: OLS [5, 4, 15], GLS [27], KF [6, 12, 4, 26, 14], Adaptive KF ...of estimation of non-stationary beta parameter in time varying extended ...estimate time varying beta using KF ... See full document
12
Covariance-based Clustering in Multivariate and Functional Data Analysis
... for covariance matrix C with respect to quadratic ...finite sample cases, and that standard covariance is almost always matched and often outperformed by S b ∗ ...shrinkage estimation ... See full document
21
Sparse Matrix Inversion with Scaled Lasso
... precision matrix in the high dimensional ...sion matrix estimation to linear regression, we construct a column estimator with the scaled Lasso, a joint estimator for the regression coefficients and ... See full document
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Contributions to high dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand
... the sample covariance in high-dimensional problems is well-known to be ...the sample covariance in a high-dimensional ...shrinkage estimation of the sample covariance ... See full document
140
The analysis of very small samples of repeated measurements II: a modified box correction
... estimated covariance structure from the estimation of the regression parameters leads to an improvement in the small sample behaviour of in- ...estimated covariance structure is not used in ... See full document
28
Band Width Selection for High Dimensional Covariance Matrix Estimation
... the sample covariance, and demonstrated that it can attain the minimax optimal ...Regularized estimation of Σ −1 has also been developed in Bickel and Levina (2008a), Cai, Liu and Luo (2011) ... See full document
36
Automatic positive semidefinate HAC covariance matrix and GMM estimation
... HAC covariance matrix estimator has been subject to some severe criticism in the literature because of their poor finite-sample properties relative to the nominal normal or chi-square asymptotic ... See full document
14
Adaptive broadband beamforming with arbitrary array geometry
... a space-time covariance matrix which explicitly incorpo- rates time lags, and a broadband steering ...a matrix or vector now is a finite impulse response filter, their transfer ... See full document
6
Limiting spectral distribution of the sample covariance matrix of the windowed array data
... reasons for this popularity are (1) this window allows to develop fast recursive algorithms which are considerably less expensive in terms of computational complexity, thereby facilitate the real-time ... See full document
15
CS-SFD Algorithm for GNSS Anti-Jamming Receivers
... of space-time adaptive processing methods have the excellent ability to suppress interferences when the space-time covariance matrix is perfectly ...the covariance ... See full document
10
An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application
... of time. The driving factors in the market are reflected in the covariance matrix as a low rank component ...residual covariance of the idiosyncratic components is often modeled by a sparse ... See full document
42
Impact of a time-dependent background error covariance matrix on air quality analysis
... error covariance matrix ...a covariance matrix is to estimate it from an ensemble of perturbed assimilation ...resulting covariance matrix is strongly time (hourly and ... See full document
16
Kernel Density Estimation for the Eigenvalues of Variance Covariance Matrix of FFT Scaling of DNA Sequences: An Empirical Study of Some Organisms
... valued time series, the concept of the spectral envelope was introduced by Stoffer et ...categorical-valued time series it is of interest to discover ways in which time series methodologies based on ... See full document
26
Autocorrelation based factor analysis and nonlinear shrinkage estimation of large integrated covariance matrix
... random matrix theories imply that there are biased extreme eigenvalues for the corresponding sample covariance matrix when the dimension of the ran- dom vectors p has the same order as the ... See full document
62
Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation
... ance matrix, which controls how the information from ob- servations spreads into the model ...background-error covariance matrix can be estimated from the ...background-error covariance ... See full document
15
Petrol and Crude Oil Prices: Asymmetric Price Transmission
... of estimation caused by the autocorrelation present was overcome by the Wald tests based on the Newey-West adjusted variance-covariance matrix with different ...the sample the test statistic ... See full document
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