Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 26 for “"sample covariance matrix"”.
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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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Applied stochastic Eigen-analysis
… to high-dimensional inference problems when the samples are drawn from a multivariate normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that dramatically outperforms that proposed by Wax and …
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Contributions to Robust Methods: Modified Rank Covariance Matrix and Spatial-EM Algorithm
… canonical 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 …
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Applied stochastic eigen-analysis
… to high-dimensional inference problems when the samples are drawn from a multivariate normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that dramatically outperforms that proposed by Wax and …
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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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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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LINEAR HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL DATA UNDER HETEROSCEDASTICITY
… hypothesis testing problems: the two-sample Behrens-Fisher problem, the heteroscedastic one-way MANOVA, and the general linear hypothesis under heteroscedasticity. Although these problems have been thoroughly studied in the classical setting, high-dimensional data make most classical …
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Bayesian Estimation of Material Properties in Case of Correlated and Insufficient Data
… in multivariate error modeling and high sample covariance matrix instability. To recover from the lack of information about the true covariance we analyze two different methodologies: first the hierarchical covariance modeling is investigated, then a method based on covariance shrinkage …
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On testing for a difference in two high-dimensional mean vectors.
… the mean vectors from two populations with equal covariance matrices.This problem is considered well-posed when the sum of the two sample sizes is greater than the data dimension and, therefore, the traditional Hotelling's T2 test can be applied. In cases where the data dimension exceeds 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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Topics in linear spectral statistics of random matrices
The behavior of the spectrum of a large random matrix is a topic of great interest in probability theory and statistics. At a global level, the limiting spectra of certain random matrix models have been known for some time. For example, the limiting spectral measure of a Wigner matrix is a …
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Enhanced minimum variance optimisation: a pragmatic approach
… into the multicriteria problem, together with covariance shrinkage – improve the performance of the MVP. The factor tilts examined include Active Distance, Concentration and Volume. Additionally, the constant correlation model is employed in the estimation of the shrinkage intensity, structured …
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Data-rich multivariable detection and diagnosis using eigenspace analysis
… control techniques that are capable of detecting covariance structure change as well as providing information about the real nature of the change occurring in the process. Eigenspace analysis is especially advantageous in data rich manufacturing processes because of its capability of reducing the …
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Physically constrained maximum likelihood (PCML) mode filtering and its application as a pre-processing method for underwater acoustic communication
… filtering is most commonly implemented using the sampled mode shape or pseudoinverse algorithms. Buck et al [1] placed these techniques in the context of a broader maximum a posteriori (MAP) framework. However, the MAP algorithm requires that the signal and noise statistics be known a priori. …
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Robust portfolio construction: using resampled efficiency in combination with covariance shrinkage
… technique focusses on estimation error in the sample covariance (one of portfolio optimisation inputs). In particular shrinkage techniques applied to the sample covariance matrix are considered and the merits thereof are assessed. The second technique considered in the thesis focusses on the …
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Physically constrained maximum likelihood (PCML) mode filtering and its application as a pre-processing method for underwater acoustic communication
… filtering is most commonly implemented using the sampled mode shape or pseudoinverse algorithms. Buck et al placed these techniques in the context of a broader maximum a posteriori (MAP) framework. However, the MAP algorithm requires that the signal and noise statistics be known a priori. Adaptive …
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Robust Mahalanobis distance in power systems state estimation
… points associated with the weighted Jacobian matrix in power system state estimation. These distances, called robust Mahalanobis distances, can be used as weight functions to robustify the residuals of both the M-estimators and the least median of squares estimators for outlier diagnostics. …
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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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Spectrum sensing, spectrum monitoring, and security in cognitive radios
… from GLRT are based on the eigenvalues of the sample covariance matrix. In-band spectrum sensing in overlay cognitive radio networks requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the …
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On some topics of financial theory
… for approximate factor structure of series of sample covariance matrices with increasing order are developed from APT research. Time/crosssection double-limit asymptotics is addressed by proposed random aggregation. It is shown that resulting small-order sample covariance matrix of the …
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