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Showing 1 to 20 of 1426 for “"covariance"”.
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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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Modeling spatial covariance functions
<p>Covariance modeling plays a key role in the spatial data analysis as it provides important information about the dependence structure of underlying processes and determines performance of spatial prediction. Various parametric models have been developed to accommodate the idiosyncratic features …
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Beta-ensembles with covariance
… and [beta]-MANOVA ensembles with diagonal covariance. These generalize the [beta]-ensembles of Dumitriu-Edelman, Lippert, Killip-Nenciu, Forrester-Rains, and Edelman-Sutton, as well as the classical [beta] = 1, 2,4 ensembles of James, Li-Xue, and Constantine. Forrester discovered a sampler …
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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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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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Thinning of point processes-covariance analyses
… two point processes by some rule. We obtain the covariance structure between the thinned processes under various thinning rules. We first obtain this structure for independent Bernoulli thinning of an arbitrary point process. We show that if the point process is a renewal (stationary or ordinary) …
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An analytic method of propagating a covariance matrix to a maneuver condition for linear covariance analysis during redezvous
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1991.
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Derivative free methods in covariance components estimation
The downhill simplex (DS), Powell's (PO), and Rosenbrock's (RO) algorithm were optimized and applied to estimation of dispersion parameters. The optimization is independent of the log-likelihood function and thus, from the model. The model can accommodate two additive genetic effects and genetic …
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Gray matter covariance networks in the mouse brain
… human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain connectivity modalities like functional and diffusion-weighted imaging, this approach can provide valuable insight into the mutual influence of regional trophic and plastic processes occurring …
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Analysis of Repeated Measures Data Under Circular Covariance
<p>Circular covariance is important in modelling phenomena in epidemiological, communications and numerous physical contexts. We introduce and develop a variety of methods which make it a more versatile tool. First, we present two classes of estimators for use in the presence of missing …
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Gini Covariance Matrix and its Affine Equivariant Version
… GMD to the multivariate case and propose a new covariance matrix so called the Gini covariance matrix (GCM). The extension is natural, which is based on the covariance representation of GMD with the notion of multivariate spatial rank function. In order to gain the affine equivariance property …
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Optimal trajectory-shaping with sensitivity and covariance techniques
… process. The state transition (sensitivity) and covariance matrices both measure the impact of plant uncertainty, and each of these mathematical constructs can be adjoined to the trajectory optimization problem to generate solutions that are less sensitive to prevalent uncertainties. A simple …
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Hebbian covariance learning and self-tuning optimal control
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1997.
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Structural RNA Homology Search and Alignment Using Covariance Models
… examples of homologous RNAs and comparing them. Covariance models: CMs) are powerful computational tools for homology search and alignment that score both the conserved sequence and secondary structure of an RNA family. However, due to the high computational complexity of their search and …
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Analysis of Growth Curves Under Some Special Covariance Structures
… derived these results by taking two types of covariance structures for Σ<sub>ij</sub>. These structures, namely equicorrelation structure and autoregressive structure, are most commonly used in the literature. For the autoregressive structure, the maximum likelihood estimator of the …
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Using evolutionary covariance to infer protein sequence-structure relationships
<p>During the last half century, a deep knowledge of the actions of proteins has emerged from a broad range of experimental and computational methods. This means that there are now many opportunities for understanding how the varieties of proteins affect larger scale behaviors of organisms, in …
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Topics in multivariate covariance estimation and time series analysis.
… analysis (RDA) is a well-known method of covariance regularization for the multivariate-normal based discriminant function. RDA generalizes the ideas of linear (LDA), quadratic (QDA), and mean-eigenvalue covariance regularization methods into one framework. The original idea and known …
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High-dimensional covariance estimation with applications to functional genomics
Covariance matrix estimation plays a central role in statistical analyses. In molecular biology, for instance, covariance estimation facilitates the identification of dependence structures between molecular variables that shed light on the underlying biological processes. However, covariance …
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