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 6 of 6 for “"Spatially correlated data"”.
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Hierarchical Gaussian Processes for Spatially Dependent Model Selection
… for nonstationary spatial fields. Large, spatially correlated data often cover a vast geographical area. However, local spatial regions may have different mean and covariance structures. Our methodology accomplishes three goals: (1) cluster locations into small regions with distinct, …
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Some Advanced Semiparametric Single-index Modeling for Spatially-Temporally Correlated Data
… the second is to develop two models for spatially correlated data; and the third is to further develop two models for spatially-temporally correlated data. To address the first topic, we propose a unified approach in its ability to simultaneously estimate the nonlinear relationship and …
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Spatial analysis of poverty and prosperity in the U.S. counties
… A spatial approach has been used to analyze the data as the data was spatially distributed. Using OLS, spatial lag, and spatial error methods, three models were developed and compared. Spatial error model explained higher percent of variation among three models. Labor markets variables were found …
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Bayesian Uncertainty Quantification while Leveraging Multiple Computer Model Runs
In the face of spatially correlated data, Gaussian process regression is a very common modeling approach. Given observational data, kriging equations will provide the best linear unbiased predictor for the mean at unobserved locations. However, when a computer model provides a complete grid of …
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Mixture Model Approaches to Integrative Analysis of Multi-Omics Data and Spatially Correlated Genomic Data
<p>Integrative genomic data analysis is a powerful tool to study the complex biological processes behind a disease. Statistical methods can model the interrelationships of the involved gene activities through jointly analyzing multiple types of genomic data from different platforms (vertical …
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Ensemble Tree-Based Machine Learning for Imaging Data
<p>In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the …