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 44 for “"correlated data"”.
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Some Advanced Semiparametric Single-index Modeling for Spatially-Temporally Correlated Data
… 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 change …
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Mixed Effects Modeling and Correlation Structure Selection for High Dimensional Correlated Data
Longitudinal data arise frequently in many studies where measurements are obtained from a subject repeatedly over time. Consequently, measurements within a subject are correlated. We address two rather important but challenging issues in this thesis: mixed-effect modeling with unspecified random …
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Model selection for correlated data and moment selection from high-dimensional moment conditions
High-dimensional correlated data arise frequently in many studies. My primary research interests lie broadly in statistical methodology for correlated data such as longitudinal data and panel data. In this thesis, we address two important but challenging issues: model selection for correlated data …
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Penalized spline modeling of the ex-vivo assays dose-response curves and the HIV-infected patients' bodyweight change
… the semi-parametric modeling approach to analyze data in health science research areas. This dissertation has three parts. The first part discusses the modeling of the dose-response relationship with correlated data by introducing overall drug effects in addition to the deviation of each …
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Energy-Aware Topology Control and Data Delivery in Wireless Sensor Networks
… two fundamental problems: topology control and data delivery. We first address energy-aware topology control taking into account throughput per unit energy as the primary metric of interest. Through both experimental observations and analysis, we show that the optimal topology is a function of …
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Distortion-Tolerant Communications with Correlated Information
… in wireless communication systems are often correlated in the spatial and/or temporal domains, and the correlation can be used to facilitate system designs and to improve system performance. First, the optimum</p> <p>node density, i.e., the optimum number of nodes in a unit area, is …
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Database engine integration and performance analysis of the BigDAWG polystore system
The BigDAWG polystore database system aims to address workloads dealing with large, heterogeneous datasets. The need for such a system is motivated by an increase in Big Data applications dealing with disparate types of data, from large scale analytics to realtime data streams to text-based …
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Multi-heuristic theory assessment with iterative selection
… our studies have identified that most of the data sets we have encountered are satiated with worthless data that actually leads to the degradation of the accuracy of machine learners. Therefore, simpler learning is more optimal. This necessitates a simpler classifier that is not confused with …
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Hierarchical Gaussian Processes for Spatially Dependent Model Selection
… 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, stationary …
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Testing for Changes in Trend in Water Quality Data
… The test is made valid for use with serially correlated data by use of a new bootstrap method titled the effective sample size bootstrap. Further issues involved in applying this test to water quality variables are also addressed.
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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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Development of Wireless Sensor Network to Detect Lameness in Dairy Cows
… Analysis is able to compress the highly correlated data of locomotion into a smaller dimension in which abnormal gaits can be differentiated.
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A Bi-variate Gamma Generalized Laplace Distribution
… model for asymmetric, heavy-tailed bivariate data. We derive the distribution's properties, including its probability density function, marginal and conditional distributions, and moments.A major contribution is the development of maximum likelihood estimators (MLEs) for BGGL parameters, with …
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Controlled training data generation with diffusion models
… generative model to produce training data specifically “useful” for supervised learning. Unlike previous works that employ an open-loop approach and pre-define prompts to generate new data using either a language model or human expertise, we develop an automated closed-loop system …
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Advanced Statistical Modeling for Model-Based Iterative Reconstruction for Single-Energy and Dual-Energy X-Ray CT
… refined statistical models including the data model and the prior model. In this dissertation, we developed an MBIR algorithm for dual-energy CT that included a joint data-likelihood model to account for correlated data noise. Moreover, we developed a Gaussian-Mixture Markov random filed …
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AFRICAN-AMERICAN MALES’ PERSPECTIVE ON PERSISTENCE DURING THEIR SECOND YEAR AT AN HBCU
… study, I collected, thematically analyzed and correlated data from participant profile sheets, focus group meetings, follow-up interviews and observational field notes. The 11 research participants provide a descriptive account of their academic experiences as they have progressed towards their …
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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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Some New Estimator in Linear Mixed Models with Measurement error
… broad range of structures including longitudinal data, repeated measures data (including cross-over studies), growth and dose-response curve data, clustered (or nested) data, multivariate data, and correlated data. In many practical situations, the observation of variables is subject to …
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Analysis of Models for Longitudinal and Clustered Binary Data
… analysis of longitudinal and clustered binary data. Such data consists of observations on a dichotomous response variable generated from multiple time or cluster points, that exhibit either decaying correlation or equi-correlated dependence. The current literature addresses modeling the …
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