covariance estimation
Shared Subspace Models for Multi-Group Covariance Estimation
... multi-group covariance estimation focus on estima- tion in terms of spectral ...developed estimation and testing procedures for the “common principal components” model, in which a set of ...
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ERROR COVARIANCE ESTIMATION IN OBJECT TRACKING SCENARIOS USING KALMAN FILTER
... 414 | P a g e deformations, variation in illumination, and partial or full occlusion of the target. In our paper the discussion is about determining the motion characteristics of moving object when encountered with ...
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High-dimensional Covariance Estimation Based On Gaussian Graphical Models
... for covariance and inverse covariance estimation and for risk consistency based on an estimated sparse graph as we mentioned ...the covariance can be computed, which was shown in Chaudhuri et ...
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Essays on financial econometrics:variance and covariance estimation using price durations
... kernel covariance estimator into a composite realized kernel where univariate realized kernels are used to estimate variances and bivariate realized ker- nels estimate ...kernel covariance estimator for ...
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Covariance estimation via sparse Kronecker structures
... The covariance matrix plays an indispensable role in multivariate data analysis and is a central quantity for estimation and ...sample covariance matrix after these observations are ...
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Large covariance estimation by thresholding principal orthogonal complements
... with estimation of high-dimensional covariance with a conditional sparsity structure, which is the composition of a low-rank matrix plus a sparse ...error covariance matrix in a multi-factor model, ...
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QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation
... strong statistical guarantees in recovering a sparse inverse covariance matrix, or alterna- tively the underlying graph structure of a Gaussian Markov Random Field, from very limited samples. We propose a novel ...
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Group Lasso Estimation of High-dimensional Covariance Matrices
... the covariance matrix Σ by expanding the process X into a dictionary of basis ...to covariance estimation is well adapted to the case of low-dimensional covariance estimation when the ...
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A Hybrid Adaptive Unscented Kalman Filter Algorithm
... state estimation based on ...noise covariance estimation of a nonlinear stochastic system, and utilize the MAP method to obtain a suboptimal and unbiased estimation for slacking down the ...
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Estimation of covariance, correlation and precision matrices for high dimensional data
... the estimation of the support of the precision matrix which corresponds to the se- lection of graphical models for Gaussian distributions [Lauritzen, ...parameter estimation, Meinshausen and B¨uhlmann ...
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Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation
... ing estimation, which was further developed by Rothman, Levina and Zhu ...sample covariance matrix directly for estimating Σ and banding the Cholesky factor matrix for estimating Σ −1 ...sample ...
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Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso
... inverse covariance regularization problem or graphical lasso with regular- ization parameter ...sample covariance graph formed by thresholding the entries of the sample covariance matrix at λ is ...
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On the estimation of covariance matrices using panel data artificial regressions
... of covariance matrices for differences between estimators when the assumption that the error term in the auxiliary model is IID is ...groups covariance matrix and the second leading to a ...
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Multichannel adaptive signal detection in space-time colored compound-gaussian autoregressive processes
... sample covariance matrix (SCM) even in compound-Gaussian ...the covariance matrix for Gaus- sian disturbance, but is no longer the MLE for compound-Gaussian ...The covariance matrix estimation ...
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A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar
... persymmetry covariance matrix estimation in the airborne MIMO radar is ...clutter covariance matrix is estimated by the original data and the constructed ...
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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 parameter. We ...
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Estimation of the mixing kernel and the disturbance covariance in IDE-based spatiotemporal systems
... the covariance is a continuous function, the spectrum is persistently exciting of infinite ...the covariance function is compactly supported or semi-compactly supported (such as with a Gaussian function) ...
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A robust fusion estimation with unknown cross covariance in distributed systems
... fusion estimation (RFE) for distributed fusion system without knowledge of the cross-covariances of sensor estimation errors is ...fusion estimation is designed to be a minimax problem, which is ...
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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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Maximum likelihood estimation of a stochastic frontier model with residual covariance
... (ML) estimation is a well-known parametric method of inference in ...estimating covariance parameters in spatial Gaussian ...(ML) estimation approach used in Cliff and Ord (1973) and Kapoor ...
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