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Showing 1 to 20 of 280 for “"Covariance Matrix"”.
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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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Contributions to Robust Methods: Modified Rank Covariance Matrix and Spatial-EM Algorithm
… correlation analysis are based on sample covariance matrix. Those moment-based techniques are optimal (most efficient) under the normality distributional assumption. They are, however, extremely sensitive to outlying observations, susceptible to small perturbation in data and poor in the …
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Monitoring for a Shift in a Process Covariance Matrix Using the Generalized Variance
… the mean vector are affected by a shift in the covariance matrix. As in the univariate case, a chart for monitoring for a change in the covariance matrix should be examined first before examining the chart used to monitor for a change in the mean vector. One such chart is the one that plots the …
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Covariance matrix estimation methods for constrained portfolio optimization in a South African setting
… to be implemented, esti- mated parameters (covariance matrix for the constrained portfo- lio) are required. The problem with these estimated parameters is that they have to be extracted from historical data based on certain assumptions. Because of the di erent estimation methods that can be …
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Robust MEWMA-type Control Charts for Monitoring the Covariance Matrix of Multivariate Processes
… normal distribution with mean vector " and covariance matrix •, but this is rarely satisfied in practice. Some robust control charts have been developed to monitor the mean and variance of univariate processes, or the mean vector " of multivariate processes, but the development of robust …
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Multivariate control charts for the mean vector and variance-covariance matrix with variable sampling intervals
… the mean vector, for monitoring variance-covariance matrix and for simultaneously monitoring the mean vector and the variance-covariance matrix of a process with a multivariate normal distribution are investigated. A variable sampling interval (VSI) feature is considered in these charts. …
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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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Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… multivariate 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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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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Examination of model uncertainty and parameter sensitivity in correlated systems using covariance structure analysis
… variable relationships is a variable correlation matrix or covariance matrix. A variable covariance matrix is a composite of variable causality, correlation, and other interactions. In other words, the variable covariance matrix contains information about the relationship of variables in a system. …
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Three Essays in Financial Economics
… The second chapter presents an improved covariance matrix estimator in the mean-variance optimization setting. Sample covariance matrix can be singular when the number of observations is less than the number of assets, and nearly singular when the number of observations exceeds the number …
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Three Essays in Finance
… factor models for forecasting stock return covariance matrix. Factor models are quite popular for forecasting covariances as they reduce the impact of the idiosyncratic return component on forecasts based on the full sample covariance matrix. I examine empirically whether introducing factor …
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CUSUM Generalized Variance Charts
… the mean vector are affected by a shit in the covariance matrix. As in the univariate case, a chart for monitoring for a change in the covariance matrix should be examined first before examining the chart used to monitor for a change in the mean vector.</p>
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On the principle of MINQUE for the estimation of variance and covariance components
… the extension for estimating the elements of a covariance matrix using this principle. The method extends easily for the case when no a priori guess of the covariance matrix is assumed. However, for incorporating the a priori guess for estimating the distinct elements of a covariance matrix, we …
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Modelling conditional covariances with orthogonal factor models
… management and monitoring is a reliable ex-ante covariance matrix of various financial time series. Therefore a reliable model which can handle a large number of time series is required to calculate an ex-ante or conditional covariance matrix.
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A partial state collocation method for covariance optimal control
… presented for two methods of incorporating the covariance in an optimal control problem. Including the covariance in the optimal control problem can be beneficial in the field of navigation where it is desirable to design trajectories which either minimize navigation error or maximize …
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Genetic evaluation and selection in multibreed populations
… theory for modelling genotypic means and covariances which are required to obtain genetic evaluation by best linear unbiased prediction (BLUP) for multibreed populations. Theory and methods for genetic evaluation and selection by BLUP using the multibreed covariance theory were presented, …
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A Monte Carlo framework for nuclear data uncertainty propagation via the windowed multipole formalism
… coefficients, and the windowed multipole covariance matrix. A sensitivity coefficient calculation algorithm based on the CLUTCH-FM methodology was implemented in OpenMC. Several methods for obtaining the windowed multipole covariance matrix from the resonance parameter covariance matrix …
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