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 1095 for “"High-Dimensional"”.
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High dimensional information processing
Part I: Consider the n-dimensional vector y = Xβ + ǫ where β ∈ Rp has only k nonzero entries and ǫ ∈ Rn is a Gaussian noise. This can be viewed as a linear system with sparsity constraints corrupted by noise, where the objective is to estimate the sparsity pattern of β given the observation vector …
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High dimensional revenue management
… for these problems must contend with (i) highly volatile demand processes that are hard to forecast, and (ii) massive scale that makes even basic optimization problems challenging. Our solutions to these problems are interesting in their own right in the areas of stochastic optimization, …
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Inferences on high-dimensional data
… of the statistical properties of the composite dimensional reduction procedures are derived."
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High-dimensional Online Changepoint Detection
… sets of unprecedented size can be collected at high frequency. This provides statisticians with new challenges in this field. In this thesis we study the online version of the changepoint detection problem in high-dimensional settings. In Chapter 1, we survey the field of changepoint detection. …
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High-dimensional Multimodal Bayesian Learning
High-dimensional datasets are fast becoming a cornerstone across diverse domains, fueled by advancements in data-capturing technology like DNA sequencing, medical imaging techniques, and social media. This dissertation delves into the inherent opportunities and challenges posed by these types of …
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High Dimensional Inference for Semiparametric Models
In the literature, high dimensional inference refers to statistical inference when
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High-dimensional data driven parameterized macromodeling
L'abstract è presente nell'allegato / the abstract is in the attachment
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Face verification using high dimensional feature
… we developed a face verification demo using high-dimensional feature. We first used Adaboost Cascade Classifier to detect face then using facial points detector get the points which we want to build the high-dimensional based on them. To the face verification problem, we used a �smart� …
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Methylation and High Dimensional Data Integration
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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Problems in high-dimensional mediation analysis
… in linear mediation models in the presence of high-dimensional mediators: estimating and inference for the indirect effect, power analysis for testing total effect, and estimation for the proportion of indirect effect.
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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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Genetic association of high-dimensional traits
… and it has become common to record multi- to high-dimensional phenotypes for individu- als. Whilst these rich datasets offer the potential to analyse complex trait structures and pleiotropic effects at a genome-wide level, novel analytic challenges arise. This thesis summarises my research …
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High-Dimensional Inference with Heterogeneous Data
… and statistical methods for inference in high-dimensional, heterogeneous generalized linear models (GLMs). In a canonical model of heterogeneous regression known as 'Mixed Sparse Linear Regression', we bring novel evidence towards the existence of a statistical price to pay for …
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Spatial Coupling for High-Dimensional Estimation
… for a variety of inference problems. For many high-dimensional regression models with unstructured designs, the Bayes-optimal estimator is computa- tionally intractable. The main idea in spatial coupling is to chain simple, unstructured measurement schemes together to obtain significant gains …
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High-dimensional econometrics and model selection
This dissertation consists of three chapters. Chapter 1 proposes a new method to solve the many moment problem: in Generalized Method of Moments (GMM), when the number of moment conditions is comparable to or larger than the sample size, the traditional methods lead to biased estimators. We propose …
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Sketching and streaming high-dimensional vectors
A sketch of a dataset is a small-space data structure supporting some prespecified set of queries (and possibly updates) while consuming space substantially sublinear in the space required to actually store all the data. Furthermore, it is often desirable, or required by the application, that the …
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Some Clustering Approaches for High-dimensional Data
This dissertation is inspired by previous clustering research in recurrent event data and genomic expression data, with the goal of incorporating new features to extend existing methods and derive meaningful conclusions. In the first framework, we proposed a nested clustering model for recurrent …
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High-dimensional classification and attribute-based forecasting
… consists of two parts. The first part focuses on high-dimensional classification problems in microarray experiments. The second part deals with forecasting problems with a large number of categories in predictors. Classification problems in microarray experiments refer to discriminating subjects …
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High-Dimensional Covariate-Dependent Gaussian Graphical Models
… norm and validate the sign consistency in the high-dimensional context. We apply our method to an influenza vaccine data set to model the gene network that evolves with time. We also investigate a Down syndrome data set to model the protein network, which varies with several covariates under a …
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Planning Practical Paths in High-Dimensional Space
… approaches have been developed to solve high-dimensional real-world path planning problems. A shortcoming of the current sampling-based algorithms is that they can obtain highly non-optimal solutions since they rely upon randomization to explore the search space. Although these planners …
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