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 90 for “"Hierarchical Models"”.
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Bayesian hierarchical models for estimating nest survival
… 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 included …
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Evidence aggregation in development economics via Bayesian hierarchical models
… 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 measures of …
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Molecular ecology and hierarchical models elucidate chronic wasting disease dynamics
Prions present a unique evolutionary scenario because a single gene codes for both a disease agent and a functionally constrained native protein. The prion precursor gene, Prnp, codes for the prion precursor protein, PrP, which is constitutively expressed as a native isoform within all mammals. …
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Accounting for structure in education assessment data using hierarchical models
… for the nesting structure. We then show that a hierarchical modeling approach allows us to appropriately account for structure in this type of data. As an illustration, we demonstrate the use of a model-based approach to comparing two teaching methods by fitting a hierarchical model to data from …
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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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The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction
… converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also …
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Bayesian Hierarchical Models for Data Extrapolation and Analysis in Rare and Pediatric Disease Clinical Trials
… 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 statistical …
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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
… 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 intake …
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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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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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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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Generalizability of health economic evaluations : methods for multinational patient-level studies
… and incremental net monetary benefit. Hierarchical modelling is applied to calculate trial-wide and country-level estimates, while accounting for clustering and incorporating country- and patient-level covariates. Results: Simon and Gail test indicated qualitative homogeneity for …
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Statistical Hierarchical Modelling for Industrial Collaborative Prognosis
… bias, whereas the independent assets- specific models are associated with high variance for the assets with sparse data. Thankfully there exist similarities due to age, upkeep, manufacturing processes, etc. across the assets that enable learning possibilities within the asset fleet, via …
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Modeling Arthropod Traits in a Bayesian Framework
… data underneath the curve. Additionally, we fit hierarchical models in two scenarios, within a genus and between genera, to explore the potential for information-sharing and generalization between species to improve curve estimation. Lastly, we extend these hierarchical models in a single-species …
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Hierarchical modelling of multivariate survival data
Hierarchical models based on conditional independence are investigated as a means of modelling multivariate survival times. The model structure follows Clayton (1978), Hougaard (1986b), and Oakes (1986, 1989). Both approximate Bayesian and maximum likelihood estimation in these models is …
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Bottom-up Methodologies in Emerging Models
… is marked by a movement from top-down hierarchical models, toward a de-centralized, bottom-up style demonstrated in recent movements. This paper examines the shift of doctrine and praxis within the American evangelical church and a simultaneous development of new methodologies in social …
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