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.
Results
Showing 1 to 20 of 42 for “"High Dimension"”.
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Dependence testing in high dimension
"The study of dependence for high dimensional data originates in many different areas of contemporary research. While a lot of existing work focuses on measuring the linear dependence and monotone dependence for fixed dimensional data, comparatively less is concerned for more complex dependence …
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Wavelet Collocation Method for Hammerstein Integral Equations of High Dimension
… and multilevel augmentation method on three dimensional Hammerstein equation with both smooth kernel and weakly singular kernel. In this part, self similar partition on d-dimensional (<em>d</em> ≥ 3) unit cube will be introduced and followed by a group of three dimensional contractive …
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The moduli space of hypersurfaces whose singular locus has high dimension
… degree I whose singular locus is at least b-dimensional. We prove that for large 1, X has a unique irreducible component of maximal dimension, consisting of the hypersurfaces singular along a linear b-dimensional subspace of P".
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Electrokinetic Methods and Applications in Australian Aquifer Settings: High-Dimension Electrical Tomography Imaging and Neural Network Filtration Techniques
… deep target for the method, particularly within highly conductive Australian geological conditions. Previously collected geophysical datasets from the Adelaide Hills have been reprocessed from two to four-dimensions utulising newly constructed algorithms, then reanalysed with supporting …
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Robust Distributed Stabilization of Interconnected Multiagent Systems
… networks as a few examples. Due to the high-dimension and complexity in configuration of these infrastructures, only a few internal variables of each agent might be measurable and the exact knowledge of the model might be unavailable for the control design purpose. The collective …
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Copulas for High Dimensions: Models, Estimation, Inference, and Applications
… four chapters that concern topics on copulas for high dimensions. Chapter 1 proposes a new general model for high dimension joint distributions of asset returns that utilizes high frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, which …
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Interpolants, Error Bounds, and Mathematical Software for Modeling and Predicting Variability in Computer Systems
… distributions of data with moderately high dimension are presented and compared with existing approaches. Novel theoretical error bounds are constructed for piecewise linear interpolants over functions with a Lipschitz continuous gradient. Finally, a mathematical software that …
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Homogenization of Partial Differential Equations with Random, Large Potential
Partial differential equations with highly oscillatory, random coefficients describe many applications in applied science and engineering such as porous media and composite materials. Homogenization of PDE states that the solution of the initial model converges to the solution to a macro model, …
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Statistical inference for high-dimensional data
… we investigate three important problems in high-dimensional statistics and develop some new methods and theory, which show the limitation of some existing approaches and motivate the use of our proposed methods. In the first chapter, we study distance covariance, Hilbert-Schmidt covariance …
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Optimization algorithms for inference and classification of genetic profiles from undersampled measurements
… 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 because the likelihood diverges. We propose a …
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Spectral properties of planar helium under periodic driving
… banded generalized eigenvalue problem of rather high dimension is solved using advanced techniques of parallel programming. In the present thesis, this theoretical/numerical machinery is employed to provide a complete description of the bound and of the doubly excited spectrum of the field-free …
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Behaviour recognition and monitoring of the elderly using wearable wireless sensors. Dynamic behaviour modelling and nonlinear classification methods and implementation.
… adaptation to a user during operation. A dimension reduction and classification scheme utilising Curvilinear Distance Analysis is further developed to deal with the recognition task presented by increasingly nonlinear and high dimension sensor readings, and anomaly detection methods …
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Model-constrained optimization methods for reduction of parameterized large-scale systems
… sample the input space, which is of high dimension for many applications of interest. This thesis proposes and analyzes a model-constrained greedy-based adaptive sampling approach in which the parametric input sampling problem is formulated as an optimization problem that targets an …
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Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01
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Model based cross-directional monitoring and control of plastic film thickness
… Teijin Films Ltd. The developed model is of high dimension and built using the first-principles of chemical and mechanical engineering, such as equations for mass transfer, heat transfer, and the flow of viscous fluids in addition to empirical knowledge related to the behaviour of polymer. …
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Hessian-based model reduction with applications to initial-condition inverse problems
… to approximate output quantities of interest of high-fidelity computational models over a wide range of input parameters play an important role in making tractable large-scale optimal design, optimal control, and inverse problem applications. We consider the problem of determining a reduced model …
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On the equivalence of sparse statistical problems
… and computationally possible in the high-dimensional setting. Sparse Principal Component Analysis (SPCA) and Sparse Linear Regression (SLR) are two problems that have a wide range of applications and have attracted a tremendous amount of attention in the last two decades as canonical …
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Bayesian Multilevel-multiclass Graphical Model
… problem focuses on the analysis of correlated high-dimensional data which has been useful in many applications. In this work, I consider a problem of detecting signals with a semiparametric regression model which can study the effects of fixed covariates (e.g. clinical variables) and sets of …
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Contributions to Robust Adaptive Signal Processing with Application to Space-Time Adaptive Radar
… First, a new adaptive diagnostic tool for high-dimension data is developed and shown robust in problematic contamination. Second, a robust data-pre-whitening method is presented based on the new diagnostic tool. Finally, a new suppression-based robust estimator is developed for use with …
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Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis
… also for accurate survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges in survival analysis. We developed a novel family of pathway-based, sparse deep neural networks (PASNet) for cancer survival analysis. PASNet family is …
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