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Showing 1 to 20 of 103 for “"Covariance matrices"”.
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Contributions to Large Covariance and Inverse Covariance Matrices Estimation
Estimation of covariance matrix and its inverse is of great importance in multivariate statistics with broad applications such as dimension reduction, portfolio optimization, linear discriminant analysis and gene expression analysis. However, accurate estimation of covariance or inverse covariance …
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A concentration inequality based statistical methodology for inference on covariance matrices and operators
… is required to take into account the covariance structure of the data during his or her analysis, which takes on the form of either a high dimensional low rank matrix or a finite dimensional representation of an infinite dimensional operator acting on some underlying function space. …
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Building a statistical linear factor model and a global minimum variance portfolio using estimated covariance matrices
Includes bibliographical references (leaves 73-75).
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Some Results on Classifying an Observation Into One of Several Multivariate Normal Populations With Equal Covariance Matrices
Made available in DSpace on 2014-12-14T14:13:55Z (GMT). No. of bitstreams: 1 8004270.pdf: 5238018 bytes, checksum: 08be13b03839904a4cd1bacf512ac69e (MD5) Previous issue date: 1979
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Covariance estimation on matrix manifolds
The estimation of covariance matrices is a fundamental problem in multivariate analysis and uncertainty quantification. Covariance matrices are an essential modeling tool in climatology, econometrics, model reduction, biostatistics, signal processing, and geostatistics, among other applications. In …
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Computational Bayesian methods applied to complex problems in bio and astro statistics.
… chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes …
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Multivariate nichemetrics
… normal distributions with heterogeneous variance-covariance matrices arc derived. The problem of estimating the measures and their precision and accuracy is investigated. Two methods, the jackknife and the bootstrap, arc described for estimating the bias and variance of an estimated measure. The …
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Topics in Sparsity and Compression: From High dimensional statistics to Overparametrized Neural Networks
… of sparsity in three different areas: covariance estimation in time-series data, linear regression with categorical variables, and neural network compression. In the first chapter, motivated by problems in computational finance, we consider a framework for jointly learning time-varying …
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The Extended Preferred Ordering Theorem for Radar Tracking Using the Extended Kalman Filter
… tracks tend to be biased - and their Kalman covariance matrices are inconsistent with the true ones. Of course, some techniques have been proposed for "debiasing" them and making their mean squared errors "consistent" with the covariance matrices determined by the tracking filter. It is shown …
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Locating facial features with active shape models
… 3. Extending the set of landmarks. 4. Trimming covariance matrices by setting most entries to zero. 5. Using other modifications such as adding noise to the training set. The resulting feature locater is shown to compare favorably with previously published methods.
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Hierarchical Gaussian models for wind field estimation and path planning
… we extract empirical estimates of the mean and covariance functions. The associated covariance matrices are anisotropic and non-stationary, and capture interactions among the wind vectors at all points in a discretization of the domain. We make the further assumption that, given a particular …
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BOOTSTRAPPING ANALOGS OF THE ONE WAY MANOVA TEST
… or differ across p groups, and assumes that the covariance matrix of each group is the same. This work suggests using the Olive (2017abc) bootstrap technique to develop analogs of one way MANOVA test. A large sample theory test has also been developed. The bootstrap tests can have considerable …
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Quantification and propagation of nuclear data uncertainties
… of the Los Alamos (LA) model. The experimental covariance matrices, not generally given in the EXFOR database, are computed using the GMA methodology used by the IAEA to establish more appropriate correlations within each experiment. Then, using systematics relating the LA model parameters …
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On Some Aspects of Model Selection Variability
… estimators. Two closed-form formulae of covariance matrices are derived for high dimensional bagging estimators, one for the nonparametric bootstrapping and the other for the parametric bootstrapping. Two simulation studies are completed in detail for demonstrating the validity of the new …
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A Bayesian model for dynamic functional connectivity estimation in the human brain with structural priors
… evaluated the ability of such a model to recover covariance matrices. The model performed well in a high dimensional, small sample simulated setting. In addition, it exhibited robustness to temporal transformations and an ability to recover simulated data generated according to both discrete and …
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Geometric Methods for Point Estimation
… work explores information geometric aspects of covariance matrix estimation. In a regular statistical model the Fisher information metric endows the parameter space with a Riemannian manifold structure. Parameter estimation can therefore also be viewed as problem in non-Euclidean data analysis. …
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Kinematic fitting of detached vertices
… in order to correct the track measurements and covariance matrices of the charged particles. The &Lgr; → ppi- and xi- → pi -&Lgr; decays were then investigated to demonstrate that the kinematic fitting routine reconstructs the decaying particles and their detached vertices correctly.
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Bayesian analysis of multivariate stochastic volatility and dynamic models
… of both the regression coefficients and the covariance matrix of the error term. Efficient parametrization of the time varying covariance matrices is studied using different modified Cholesky decompositions. We propose a hierarchal approach for selection of the volatility equation's variance …
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Bayesian and Information-Theoretic Learning of High Dimensional Data
… In the Bayesian Graphical LASSO, the inverse covariance matrix of the data distribution is assumed to be sparse, inducing a sparsely connected Gaussian graph. In the nonparametric Mixture of Factor Analyzers, the covariance matrices in the Gaussian Mixture Model are forced to be low-rank, …
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Multifidelity Covariance Estimation Three Ways
… a suite of three methods for multifidelity covariance estimation. We begin with a straightforward extension of scalar multifidelity Monte Carlo to matrices, obtaining what we refer to as the Euclidean or linear control variate mutifidelity covariance estimator. The mean squared error of this …
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