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 61 for “"Bayesian method"”.
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DATA-DRIVEN BAYESIAN METHOD-BASED TRAFFIC CRASH DRIVER INJURY SEVERITY FORMULATION, ANALYSIS, AND INFERENCE
… from prior information and studied datasets, Bayesian models are efficient methods in data analysis with more accurate results, but their applications in traffic safety studies are still limited. By examining the driver injury severity patterns, this research is proposed to systematically …
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A Bayesian approach to crossed-random-effects mediation analysis for zero-inflated mediators and binary outcomes
… the current study investigated whether Bayesian estimation can be a viable alternative as suggested by previous research. The simulation results indicated that Bayesian estimates were essentially unbiased and precise. There were only two out of 180 models did not converge; future studies …
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Approximate Bayesian approaches and semiparametric methods for handling missing data
… In the first paper (Chapter 2), an approximate Bayesian approach is developed to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the outcome variable. The proposed Bayesian method is also extended to incorporate the …
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A Statistical Model to Determine Multiple Binding Sites of a Transcription Factor on DNA Using ChIP-seq Data
… in genome with superior accuracy. Although many methods have been proposed to find binding sites for ChIP-seq data, they can find only one binding site within a short region of the genome. In this study we introduce a statistical model to identify multiple binding sites of a transcription factor …
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A case study in robust quickest detection for hidden Markov models
… rare events in a real data set. The first method is a dynamic programming based Bayesian approach, and the second is a non-Bayesian approach based on the cumulative sum algorithm. We discuss implementation considerations for each method and show their performance through simulations for a …
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Semiparametric Bayesian Joint Model With Variable Selection
… survival or time-to-event histories. Recently, methods for jointly modeling longitudinal and survival data have gained popularity in the statistical literature. In this dissertation, we consider the problem of variable selection in a joint modeling framework where longitudinal and survival data …
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Random Effects Selection In Bayesian Accelerated Failure Time Model With Correlated Interval Censored Data
… crucial in such applications. We propose a Bayesian method for random effects selection in mixed effects accelerated failure time models. The proposed method relies on Cholesky decomposition on the random effects covariance matrix and the parameter expansion method for the selection of …
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Estimating time delays between irregularly sampled time series
… area of astrophysics. Lensing is the most direct method of measuring the distribution of matter, which is often dark, and the accurate measurement of time delays set the scale to measure distances over cosmological scales. For our purposes, this means that we have to estimate a time delay between …
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Statistical methods for fMRI data analysis
… pose some challenges to traditional statistical methods which focus on data with smal sample size and simple data structure. The functional activation detection and functional connectivity network analysis by using fMRI are two important research topics in the neuroscience. In this work, we …
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Extraction and usage of crystallographic knowledge for refinement and validation of molecular models /
… chemically similar observations can be used for Bayesian method-based outlier detection: previously unseen, or seen relatively rarely, geometric observations in molecules in consideration are spotted and marked for further analysis. Software implementing this principle has been developed and a …
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Bayesian hierarchical modelling of dual response surfaces
Dual response surface methodology (Vining and Myers (1990)) has been successfully used as a cost-effective approach to improve the quality of products and processes since Taguchi (Tauchi (1985)) introduced the idea of robust parameter design on the quality improvement in the United States in …
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Essays in Computational Econometrics
… challenges. Each essay in this thesis presents methods that relate to computational problems arising in econometric theory or applications. In the first essay, an improved Bayesian method for probabilistic record linkage in large matching problems, with an accompanying implementation in R/C++, …
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Decision support tools for urban air quality management
… One of the principal thesis contributions is a Bayesian method to exploit the asymmetry between the rich aerosol dataset and the relatively poor dataset on gas-phase precursors. A Markov Chain Monte Carlo algorithm was combined with the equilibrium inorganic aerosol model ISORROPIA to produce a …
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Contributions to quality improvement methodologies and computer experiments
This dissertation presents novel methodologies for five problem areas in modern quality improvement and computer experiments, i.e., selective assembly, robust design with computer experiments, multivariate quality control, model selection for split plot experiments, and construction of minimax …
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Response Adaptive Design using Auxiliary and Primary Outcomes
… and primary endpoints is established through Bayesian method. We extend parameter space from one dimension to two dimensions, say primary and auxiliary efficacies, by implementing a conditional weigh function on the loss function of the design. The allocation ratio is updated at each stage by …
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Testing New Weak Lensing Measurement Techniques With The Dark Energy Survey
… taken during high wind. We then present the methods and validation of two new techniques in weak lensing shear and magnification measurement. We demonstrate highly accurate recovery of weak gravitational lensing shear using an implementation of the Bayesian Fourier Domain (BFD) method, …
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A Comparative Analysis of Bayesian Nonparametric Variational Inference Algorithms for Speech Recognition
Nonparametric Bayesian models have become increasingly popular in speech recognition tasks such as language and acoustic modeling due to their ability to discover underlying structure in an iterative manner. These methods do not require a priori assumptions about the structure of the data, such as …
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Investigating the performance of process-observation-error-estimator and robust estimators in surplus production model: a simulation study
… with normal distribution. This study used Bayesian method, revised Metropolis Hastings within Gibbs sampling algorithm (MHGS) that was previously used to solve POE_N (Millar and Meyer, 2000), developed the MHGS for the other estimators, and developed the methodologies which enabled all the …
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Enhancing the Data, Information and Knowledge from Proteomics Experiments through Biomedical Informatics
… Also, parametric and non-parametric statistical methods for assigning significance are applied here to determine the key proteomic changes between proteomes and enhance knowledge discovery. Further, an empirical Bayesian method of combining multiple proteomics experiments as an application of …
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Methods for the identification and optimal exploitation of profitable betting scenarios
… collection of model outputs. It is shown that a Bayesian method can be constructed to derive accurate bias estimates, even when the model outputs are merely a collection of independent Bernoulli trials. In addition, the method is expanded, to allow the quantification of a time-varying bias, as …
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