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 10 of 10 for “"High-Dimensional Time Series"”.
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Efficient search methods for high dimensional time-series
… developing efficient methodology for analysing high dimensional time-series, with an aim of detecting structural changes in the properties of the time series that may affect only a subset of dimensions. Firstly, we develop a Bayesian approach to analysing multiple time-series with the aim of …
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HIGH DIMENSIONAL TIME SERIES ANALYSIS AND ITS APPLICATION IN MODELING TRANSMISSION DYNAMICS OF DENGUE
… of white noise (WN) is an essential step in time series analysis. In a high dimensional set-up, most existing methods either are computationally infeasible, or suffer from highly distorted Type-I errors, or both. To address this problem, we propose an easy-to-implement bootstrap method for …
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A study of kNN using ICU multivariate time series data
… is the processing of ICU multivariate and high dimensional time-series data collected at irregular time periods. To handle the ICU irregular multivariate time-series three different methods were developed: Capture Statistics, Detect Changes, and Aggregate Segments. We examine the …
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Iterative Monte Carlo Approximations for Bayesian Inference
… distribution. Chapter 5 adopts ideas from high-dimensional time series to efficiently tackle the difficult setting where we cannot evaluate the density of the target distribution and instead can only generate synthetic data. Chapter 6 explores the use of a scalable Hessian approximation in …
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Multivariate Nonstationary Time Series: Spectrum Analysis and Dimension Reduction
… problems in modern multivariate nonstationary time series analysis: spectrum analysis and dimension reduction. The first part of the dissertation introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an …
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Data-Driven Pipeline for Learning Discrete behavioural Models of Cyber-Physical Systems
… recurring steps. In the discretization stage, high-dimensional time series data are converted into event traces without requiring labelled ground truth. A non-parametric change point detection algorithm identifies statistical changes in sensor readings or system parameters, which are treated as …
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Network Analysis of the Financial Sector: A Comprehensive Perspective with Adaptive Joint LASSO Method
… and the onset of the Russo-Ukrainian war highlighted the importance of understanding shock spillovers for policymakers and academics alike. Specifically, shock transmissions in financial network settings warrant attention to support regulatory or policy interventions for effectively …
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Spectral methods and computational trade-offs in high-dimensional statistical inference
… popular in designing fast algorithms for modern highdimensional datasets. This thesis looks at several problems in which spectral methods play a central role. In some cases, we also show that such procedures have essentially the best performance among all randomised polynomial time algorithms by …
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Corporate Default Predictions and Methods for Uncertainty Quantifications
… the corporate default risks based on large-scale time-to-event and covariate data in the context of controlling credit risks. Specifically, we propose a competing risks model to incorporate exits of companies due to default and other reasons. Because of the stochastic and dynamic nature of the …
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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 …