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 31 for “"Bayesian Hierarchical Models"”.
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Bayesian hierarchical models for estimating nest survival
… accuracy based on AIC results. Next we extended Bayesian Hierarchical Model to include different nest period lengths which estimated the overall survival rates and survival curves with combined nest period lengths. For unknown nest fate, the nest fate effect and the nest-specific covariates were …
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Evidence aggregation in development economics via Bayesian hierarchical models
… This thesis performs evidence aggregation using Bayesian hierarchical models, which both aggregate evidence and assess the true underlying heterogeneity across settings, for applications in development economics. Where necessary, the thesis develops new methods to aggregate evidence on certain …
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An Individualized Allocation Algorithm for Use with Bayesian Hierarchical Models
Statistical models have widespread use in data science, and while some datasets can be mod- eled well using one model other applications may require multiple models to accurately capture mechanisms within different subgroups of the dataset. Which subjects are assigned to each model in turn impacts …
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Bayesian Hierarchical Models for Data Extrapolation and Analysis in Rare and Pediatric Disease Clinical Trials
… draw conclusions for the pediatric population. Bayesian hierarchical modeling facilitates the combining (or ``borrowing") of information across disparate sources, such as adult and pediatric data. In this thesis we begin by developing, illustrating, and providing suggestions for Bayesian …
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Using Bayesian Hierarchical Models to Study the Spatial and Temporal Distribution of Mammals in Serengeti National Park
… were observed during the year 2012. Occupancy models are used to analyze this type of data, but they require discretization of time. Thus, we have to choose the width of time intervals to be analyzed. The goal of this research is to develop methodology to compare results based on different time …
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Quantification of Variability, Abundance, and Mortality of Maumee River Larval Walleye (Sander vitreus) Using Bayesian Hierarchical Models
… and their role in Lake Erie walleye recruitment. Bayesian hierarchical models were used to quantify spatial and temporal variability, and estimate abundance and mortality within the river while accounting for spatial and temporal uncertainty. We sampled larval walleye at the river mouth and in the …
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Statistical Monitoring and Modeling for Spatial Processes
Statistical process monitoring and hierarchical Bayesian modeling are two ways to learn more about processes of interest. In this work, we consider two main components: risk-adjusted monitoring and Bayesian hierarchical models for spatial data. Usually, if prior information about a process is …
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Spatial and Spatiotemporal Modeling of Epidemiological Data
… the study. Chapter two describes the regression models commonly used in spatial data analysis. Various types of regression methods such as OLS, GWR and MGWR were used to study the association between diabetes prevalence and socioeconomic and lifestyle factors on county level data of Midwestern …
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Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data
Bayesian hierarchical models are useful for modeling spatial data because they have flexibility to accommodate complicated dependencies that are common to spatial data. In particular, intrinsic conditional autoregressive (ICAR) models are commonly assigned as priors for spatial random effects in …
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Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
… approaches are available: one applies separate models for different regions; the other applies hierarchical models. The separate modeling approach has two major difficulties: first, we often do not know the underlying clustering structure of the entire data; second, it usually ignores possible …
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Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology
<p>In this dissertation, the flexibility of Bayesian hierarchical models specified using a latent Gaussian Markov Random Field (GMRF) are evaluated for use in analyzing large complex spatial and spatio-temporal data with the goal of contributing to an interdisciplinary effort of developing an …
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Bayesian analysis of spatial and survival models with applications of computation techniques
… discusses the methodologies of applying Bayesian hierarchical models to different data with geographical characteristics or with right-censored failure time. A conditional autoregressive (CAR) prior is used for the model to capture spatial effects. Markov chain Monte Carlo (MCMC) methods …
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Separate and Joint Analysis of Longitudinal and Survival Data
… survival. Our project emphasizes the usage of Bayesian Hierarchical Models and Win-BUGS to jointly model the survival data and the longitudinal data—mass. The results of the joint analysis indicate that the use of ultrasound and water-soluble microcapsules have no negative effect on survival. …
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Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data
… outcomes from heterogeneous populations using Bayesian survival kernel models. By connecting kernel machines with semiparametric Bayesian hierarchical models, the proposed unified model frameworks can identify significant elements as well as sets regardless of mis-specifications of …
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Combining measurements with deterministic model outputs: predicting ground-level ozone
… how to combine model outputs from deterministic models with measurements from monitoring stations for air pollutants or other meteorological variables. We consider two different approaches to address this particular problem. The first approach is by using the Bayesian Melding (BM) model proposed …
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Extracting more wisdom from the crowd
… such method can fail even if based on perfect Bayesian estimates of individual confidence, or, more generally, on Bayesian posterior probabilities. Our model suggests a new method for aggregating opinions: select the answer that is more popular than people predict. We derive theoretical …
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Innovative Multivariate Meta-Analyses Methods For Diagnostic Tests And Multiple Treatments
… assumptions on perfect specificity and develop Bayesian hierarchical models that provide simultaneous estimates of sensitivities, specificities, and disease prevalence with adjustments for study-level covariates. We demonstrate the model performance using the same published dataset on COVID …
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Statistical methods for longitudinal medical data with applications
… data. They range from generalized mixed- effect models, growth and evolution modeling (often combined with the mixed- effects structures), to time-to-events analyses. However, such statistical methodologies might sometimes involve complicated issues to deal with, especially those related to …
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Feasibility of Using Virtual Unenhanced Images to Replace Pre-Contrast Images In Multiphase Renal Ct Examinations
… and the differences were tested using a Bayesian Hierarchical model. VUE images were found to be inferior to TUE images for visualization of major vessels and depiction of liver parenchyma. CT numbers were measured in the liver, spleen, spine, aorta, cystic lesions, subcutaneous fat, …
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Extending Space-Time Putative Hazard Models to Detect Latent Risk Features
… novel development of three different space-time Bayesian hierarchical modeling methods. </p><p>In Part I, we have addressed a fundamental problem in the analysis of small area health outcomes data, when intermittent operation of facilities could lead to evidence for latent periods of risk. This …
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