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 44 for “"Bayesian Hierarchical Model"”.
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Modeling Transition Probabilities for Loan States Using a Bayesian Hierarchical Model
A Markov Chain model can be used to model loan defaults because loans move through delinquency states as the borrower fails to make monthly payments. The transition matrix contains in each location a probability that a borrower in a given state one month moves to the possible delinquency states the …
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Bayesian Analysis of Discrete Longitudinal Data
This thesis explores a Bayesian hierarchical model to compare treatment effectiveness for menopausal symptom relief. Specifically, this model recognizes the discrete nature of the data, as well as its time dependency. Bayesian analysis is used to make inference on each individual profile, as well …
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Bayesian Hierarchical Modeling for Longitudinal Frequency Data
… research is to develop a longitudinal frequency model for data collected regularly for several individuals over an extended time period. This model must recognize explicitly the discrete nature of the data, as well as any dependence that exists among an individual's time consecutive measurements. …
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Star Formation History of the Milky Way Galaxy Disk Using a Hierarchical Model
… and large data sets. For this research, the Bayesian hierarchical model is used to estimate the age of the WDs. The Bayesian approach has already proved its credibility in various fields. The Bayesian hierarchical model produces parameter estimators for individual objects. It uses the …
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STATISTICAL MODELS AND INFERENCE FOR LI-ION BATTERY PROGNOSTICS
… years. This thesis proposes three statistical models and inference for the Li-ion battery prognostics based on the easy to measure operational profiles. The three models include a Bayesian hierarchical model which is good at long term predictions of battery degradation state, a state space …
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Bayesian Integrative Analysis of Omics Data
… like cancer. This dissertation focuses on Bayesian methodology establishment in integrative analysis of radiogenomics and pathway driver detection applied in cancer applications. We initially present Radio-iBAG that utilizes Bayesian approaches in analyzing radiological imaging and …
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Ensemble forecasting in the Mediterranean sea
… of the surface wind field derived from a Bayesian Hierarchical Model (BHM). The ocean members are forced with samples from the posterior distribution of the wind during the assimilation of satellite and in-situ ocean data. The initial condition perturbations are then consistent with the …
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Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
… it is convenient, the conventional single model approach may fail to accurately describe the relationships between variables. Two alternative modeling approaches are available: one applies separate models for different regions; the other applies hierarchical models. The separate modeling …
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Probabilistic Models for Human Migration Forecasting and Residency Imputation
I develop probabilistic models to enhance the estimation and forecasting of human migration flows and residency. Using a Bayesian hierarchical approach, I first propose a model for forecasting global bilateral migration flows among the 200 most populous countries, producing well-calibrated …
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Bayesian and Frequentist Approaches for the Analysis of Multiple Endpoints Data Resulting from Exposure to Multiple Health Stressors.
… chemicals. It consists of fitting a mathematical model to the exposure data and the BMD is the dose expected to result in a pre-specified response or benchmark response (BMR). Most available exposure data are from single chemical exposure, but living objects are exposed to multiple sources of …
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Bayesian hierarchical models for estimating nest survival
… widely used likelihood-based logistic regression model was evaluated in the first part of the dissertation. In this part, we investigated the importance of nest age in estimating survival rates and measured the model selection accuracy based on AIC results. Next we extended Bayesian Hierarchical …
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Bayesian adaptive methods to incorporate preclinical data into phase I clinical trials
… to fill the gap by providing solutions in the Bayesian paradigm, with purposes of improving the design and analysis of adaptive phase I dose-escalation trials. Specifically, our focus is on the transition step of early drug development, where phase I clinical trials are preceded only with some …
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Haplotype-Based Approaches For The Study Of Human Evolution
… variants. The second approach uses a Bayesian hierarchical model to assign variants to mixture proportions of identical-by-descent or recurrent variants. By identifying recurrent mutations, we can better understand the spectrum of recent mutations in human populations, the source of …
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Contributions to modeling parasite dynamics and dimension reduction
For my thesis, I have worked on two projects: modeling parasite dynamics (Chapter 2) and complementary dimensionality analysis (Chapter 3). In the first project, we study a longitudinal data of infection with the parasite Giardia lamblia among children in Kenya. Understanding the infection and …
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Statistical approaches for spatial prediction and anomaly detection
… likelihood with an empirical likelihood in the Bayesian hierarchical model, approximate posterior distributions for the mean and covariance parameters can be obtained. Due to the complex nature of the hierarchical model, standard Markov chain Monte Carlo methods cannot be applied to sample from …
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Characterizing methane emissions on oil and gas sites
… single-source inversion method to a fully Bayesian hierarchical model for inferring multi-source emission characteristics. This model makes it possible to use point sensor networks for accurate emission localization and quantification on complex sites where it is common for multiple sources …
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Bayesian and geostatistical analysis of the effect of air pollution on asthma hospitalisation in Perth
… hospitalisation through building a mathematical model which calculates the risk of asthma hospitalisation for Perth metropolitan areas. The data used in this study are: averaged emission inventory data (NO, CO and PM10) in 2006 and records of asthma hospitalisation for 75 postcodes in the Perth …
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Bayesian Adaptive Clinical Trial Design
… <p>In this dissertation, we propose Bayesian adaptive clinical trial designs to address these challenges. Specifically, we propose (a) a novel Bayesian dose-finding design to find the OBD of drug combination based on risk-benefit tradeoff, (b) a Bayesian adaptive design that …
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Bayesian Approaches for the Mechanistic Analysis of Protein Aggregation Kinetics
… this work, I have demonstrated how mathematical modelling based on chemical kinetics can be used to overcome current challenges in deducing the microscopic mechanism of amyloid formation, in particular in the context of how external perturbations may affect this process. Furthermore, I extended …
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Statistical learning for decision making : interpretability, uncertainty, and inference
Data and predictive modeling are an increasingly important part of decision making. Here we present advances in several areas of statistical learning that are important for gaining insight from large amounts of data, and ultimately using predictive models to make better decisions. The first part of …
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