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 247 for “"High Dimensional Data"”.
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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 data driven parameterized macromodeling
L'abstract è presente nell'allegato / the abstract is in the attachment
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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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Statistical inference for high-dimensional data
… to deduce properties of the underlying data generating process. In this thesis, 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 …
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Some Clustering Approaches for High-dimensional Data
… 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 event data to capture the …
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Stereotype Logit Models for High Dimensional Data
… the state of progression using gene expression data. One challenge when modeling microarray gene expression data is that there are more genes (variables) than there are observations. In addition, the genes usually demonstrate a complex variance-covariance structure. Therefore, modeling a …
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Statistical Analysis of Structured High-dimensional Data
High-dimensional data such as multi-modal neuroimaging data and large-scale networks carry excessive amount of information, and can be used to test various scientific hypotheses or discover important patterns in complicated systems. While considerable efforts have been made to analyze …
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Bayesian Modeling of Complex High-Dimensional Data
With the rapid development of modern high-throughput technologies, scientists can now collect high-dimensional complex data in different forms, such as medical images, genomics measurements. However, acquisition of more data does not automatically lead to better knowledge discovery. One needs …
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Efficient similarity search in high-dimensional data spaces
Similarity search in high-dimensional data spaces is a popular paradigm for many modern database applications, such as content based image retrieval, time series analysis in financial and marketing databases, and data mining. Objects are represented as high-dimensional points or vectors based on …
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Novel Network-Based Models for High-dimensional Data
Modern cancer-genomic studies frequently involve high-dimensional omics data characterized by complex network structures, such as gene pathways in transcriptomic profiles and protein-protein interaction networks in proteomic profiles. These network structures, encompassing network topol ogy, edge …
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Beyond Limits: Detecting Anomalies in Sparse, High-dimensional Data
Anomaly detection is a critical aspect of data-driven decision-making, particularly in high-stakes areas such as fraud detection and identifying manufacturing defects. However, the proprietary nature and specialized use cases of such data often result in data that is both high-dimensional and has …
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Statistical Methods for Complex and/or High Dimensional Data
… and implementation of statistical methods for high-dimensional and/or complex data, with an emphasis on $p$, the number of explanatory variables, larger than $n$, the number of observations, the ratio of $p/n$ tending to a finite number, and data with outlier observations. First, we propose a …
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Ensemble of Feature Selection Techniques for High Dimensional Data
<p>Data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships from large amounts of data stored in databases, data warehouses, or other information repositories. Feature selection is an important preprocessing step of data mining that helps …
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Robust machine learning models for high dimensional data interpretation
L'abstract è presente nell'allegato / the abstract is in the attachment
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LINEAR HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL DATA UNDER HETEROSCEDASTICITY
… In this thesis, we mainly consider three high-dimensional 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 …
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Bayesian and Information-Theoretic Learning of High Dimensional Data
… of sparseness is harnessed to learn a low dimensional representation of high dimensional data. This sparseness assumption is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlated features are identified for the response variable. In the sparse Factor Analysis …
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Error Correction for High-Dimensional Data via Convex Programming
… to achieving excellent performance on public databases, this approach sheds light on several important issues in face recognition, such as the choice of features and robustness to corruption and occlusion.
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Contributions to classification and calibration with high-dimensional data
Statistical classification and calibration with high-dimensional data are studied. We have proposed new classification and calibration procedures for high-dimensional data and have established dimensional consistency for certain high-dimensional classification and calibration procedures. Strong …
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Statistical inference in high dimensional data and machine learning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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