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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 30 for “"Covariance Estimation."”.
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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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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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Essays on financial econometrics : variance and covariance estimation using price durations
Asset variance and covariance are fundamental for financial risk management and many finance applications. With the advent of tick-by-tick high-frequency data, the estimation of univariate variances and multivariate covariance matrices has attracted more attention from econometricians. Many of the …
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Semi-Supervised Anomaly Detection and Heterogeneous Covariance Estimation for Gaussian Processes
… partially observed and potentially non-definite covariance matrix into a low dimensional manifold. The projection is estimated in such a way that positively correlated sensors are close to each other and negatively correlated sensors are distant. We then fit a Gaussian process given these …
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Learning Gaussisan noise models from high-dimensional sensor data with deep neural networks
While measurement covariances are often taken to be constant in many robotic state estimation systems, many sensors exhibit different interactions with their environment. Accurate covariance estimation allows graph-based estimation techniques to better optimize state estimates by reasoning about …
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An Analysis Using State Space Global Coherence of Brain Dynamics in a Young Child Under Sevoflurane General Anesthesia
… the data, while the other first estimated the covariance matrix and then used this matrix to compute GC. The SSGC approaches were first applied to a validation dataset that had been previously analyzed using SSGC for covariance estimation. This was done to ensure that my analysis was …
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Cluster Analysis in High Dimensions: Robustness, Privacy, and Beyond
… cluster: this encompasses problems such as mean estimation and covariance estimation, which correspond to learning the location and shape of a cluster, respectively. In this thesis, we study various classic problems in high-dimensional cluster analysis, relating to both identifying several …
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Robust estimation, regression and ranking with applications in portfolio optimization
… efficient methods for robust mean-covariance estimation and robust linear regression using special mathematical programming models and semi-definite programming (SDP). In the robust covariance estimation problem, we design an optimization model with a loss function on the weighted …
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A non-convex framework for structured non-stationary covariance recovery theory and application
… The thesis develops a structured time-indexed covariance model for non-stationary time-series data by decomposing them into sparse spatial and temporally smooth components. Traditionally, time-indexed covariance models without structure require a large sample size to be estimable. While the …
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Think global, act local when estimating a sparse precision matrix
Substantial progress has been made 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 …
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Optimization algorithms for inference and classification of genetic profiles from undersampled measurements
… SMURC: Small-sample MUltivariate Regression with Covariance estimation. Specifically, we consider a high dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. We show that, in this case, the maximum likelihood approach is senseless …
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Cure Rate Model with Spline Estimated Components
… using a smooth EM algorithm. Louis' formula for covariance estimation in an EM algorithm is generalized to yield point-wise confidence intervals for both functions. A simple model selection procedure based on the Kullback-Leibler geometry is derived for the proposed cure rate model. Numerical …
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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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Communication-Aware, Scalable Gaussian Processes for Decentralized Exploration
… model. The methodology consists of two steps: i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters; and ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased …
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Integrating Principal Component Analysis and Deep Learning Methods for Data Representation and Image Denoising
… First, PCA is derived via standardization, covariance estimation, and eigenvalue, eigenvector decomposition; component selection is guided by scree plots and cumulative variance. We demonstrate the PCA using the Wine Dataset (178 samples), that provides outcomes from a chemical assessment of …
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Principled approaches to robust machine learning and beyond
… similarly inspired, algorithmic paradigms for estimation in high dimensions in the presence of a small number of adversarially added data points. Both algorithms are the first efficient algorithms which achieve (nearly) optimal error bounds for a number fundamental statistical tasks such as …
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