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Showing 1 to 12 of 12 for “"Precision matrix"”.
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Think global, act local when estimating a sparse precision matrix
… in the estimation of sparse high dimensional precision matrices from scant datasets. This is important because precision matrices underpin common tasks such as regression, discriminant analysis, and portfolio optimization. However, few good algorithms for this task exist outside the space of …
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Model-based methods for high-dimensional multivariate analysis
… analysis model when the predictor is matrix valued. We simultaneously estimate the means and the precision matrix, which we assume has a Kronecker product decomposition. Our penalties encourage pairs of response category mean matrix estimators to have equal entries and also encourage …
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Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… normal distributions with common covariance matrix. The goal for this part is to derive objective/non-informative priors for the parameterizations and use these priors to build up constructive random posteriors of the Kullback-Liebler (KL) divergence of the two multivariate normal …
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Inference of high-dimensional linear models with time-varying coefficients
… 2, we propose an algorithm for covariance and precision matrix estimation high-dimensional transpose-able data. The method is based on a Kronecker product approximation of the graphical lasso and the application of the alternating directions method of multipliers minimization. A simulation …
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Three essays on econometrics: Network estimators with applications and assessment of the effects of Covid-19 pandemic
… dependencies can be obtained from the precision matrix, which is the inverse of the covariance matrix. Furthermore, by a proper re-scaling of this matrix, it is possible to derive a weighted network of partial correlations. Three estimators are proposed and their performances are …
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Studies of UHPLC-MS performance and application to rapid sensitive and robust drug analysis
… pH RPLC method in terms of linearity, accuracy, precision, matrix effects and sensitivity. Finally, a switching system comprising two different, complementary stationary phase materials is designed and evaluated to widen the elution window, allowing for the simultaneous analysis of both polar and …
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Optimization Methods for Machine Learning under Structural Constraints
… Models, which aims to estimate a sparse precision matrix from iid multivariate Gaussian samples. We propose a novel estimator via ℓ₀ℓ₂-penalized pseudolikelihood. We then design a specialized nonlinear Branch-and-Bound (BnB) framework that solves a mixed integer programming (MIP) …
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Models and methods for computationally efficient analysis of large spatial and spatio-temporal data
… on a fine grid and thus enables the posterior precision matrix to be diagonal through introducing a missing data scheme. This results in parameter estimation and spatial interpolation simultaneously under the Bayesian Markov chain Monte Carlo (MCMC) framework.</p><p>The EAR model is naturally …
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SPDE-derived random fields in structural optimisation and elastodynamics
… For a random field with Matérn covariance, the precision matrix (the inverse of the covariance matrix) corresponds to the finite element stiffness matrix of a potentially fractional PDE involving a second-order elliptic operator. By discretising the PDE using finite element methods on the …
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Efficient Bayesian analysis of spatial occupancy models
… which include exploiting the sparsity of the precision matrix of the ICAR model and also making use of Polya-Gamma latent variables to obtain closed form expressions for the posterior conditional distributions of the parameters of interest. An algorithm for efficiently sampling from the …
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Adaptive sampling for spatial prediction in wireless sensor networks
… the spatial field exploiting sparsity of the precision matrix. A new GMRF optimality criterion for the adaptive navigation problem is also proposed such that computational complexity of a greedy algorithm to solve the resulting optimization is deterministic even with increasing number of …
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Joint Gaussian Graphical Model for multi-class and multi-level data
… between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate …