Results 31 to 40 of about 531,665 (271)
Frontiers in Applied Mathematics and Statistics, 2022 This work proposes a robust and non-Gaussian version of the shrinkage-based knowledge-aided EnKF implementation called Ensemble Time Local H∞ Filter Knowledge-Aided (EnTLHF-KA).
Santiago Lopez-Restrepo +9 more
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Kronecker Sum Decompositions of Space-Time Data [PDF]
, 2013 In this paper we consider the use of the space vs. time Kronecker product decomposition in the estimation of covariance matrices for spatio-temporal data.
Greenewald, Kristjan +2 more
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Covariate assisted screening and estimation
The Annals of Statistics, 2014 Consider a linear model Y = Xβ + z, where X = Xn;p and z ≈ N(0; In). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series [11] and change point problem [2], we are primarily interested in the case where the Gram matrix G = X1X is
Ke, Zheng Tracy +2 more
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Generalized sparse covariance-based estimation [PDF]
Signal Processing, 2018 In this work, we extend the sparse iterative covariance-based estimator (SPICE), by generalizing the formulation to allow for different norm constraints on the signal and noise parameters in the covariance model. For a given norm, the resulting extended SPICE method enjoys the same benefits as the regular SPICE method, including being hyper-parameter ...
Swärd, Johan +2 more
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IEEE Access, 2021 In order to solve the problem that the gridless DOA estimation algorithms based on generalized finite rate of innovation (FRI) signal reconstruction model are not suitable for two-dimensional DOA estimation using planar array, a separable gridless DOA ...
Kunda Wang, Lin Shi, Tao Chen
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Efficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood [PDF]
, 2012 We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal components analysis (PCA) does not efficiently estimate the factor ...
Bai, Jushan, Liao, Yuan
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Maximum Likelihood Estimation for Linear Gaussian Covariance Models [PDF]
, 2016 We study parameter estimation in linear Gaussian covariance models, which are $p$-dimensional Gaussian models with linear constraints on the covariance matrix.
Richards, Donald +2 more
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The Minimum Regularized Covariance Determinant Estimator [PDF]
SSRN Electronic Journal, 2017 The Minimum Covariance Determinant (MCD) approach robustly estimates the location and scatter matrix using the subset of given size with lowest sample covariance determinant. Its main drawback is that it cannot be applied when the dimension exceeds the subset size. We propose the Minimum Regularized Covariance Determinant (MRCD) approach, which differs
Boudt, Kris +3 more
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Variational Bayesian Parameter Estimation Techniques for the General Linear Model
Frontiers in Neuroscience, 2017 Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data.
Ludger Starke +3 more
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Adaptive covariance matrix estimation through block thresholding [PDF]
, 2012 Estimation of large covariance matrices has drawn considerable recent attention, and the theoretical focus so far has mainly been on developing a minimax theory over a fixed parameter space.
Cai, T. Tony, Yuan, Ming
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