Global ETD Search
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Showing 1 to 3 of 3 for “"Count Regression Models"”.
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Count regression models with a misclassified binary covariate : a Bayesian approach.
… are inevitable in a variety of regression applications. Fallible measurement methods are often used when infallible methods are either expensive or not available. Ignoring mismeasurement will result in biased estimates for the associated regression parameters. The models …
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Using biomarker data to monitor the HIV epidemic
… and bivariate non-linear mixed-effects models are implemented in a fully Bayesian framework. A simulation study is conducted to investigate the biomarkers’ features that affect the accuracy of the estimation of recency. The research findings suggest that rapidly evolving biomarkers of …
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Count-Regression-Based Empirical Causal Analysis from a Potential Outcomes Perspective: Accounting for Boundedness, Discreteness, Dispersion and Unobservable Confounding
… In this dissertation, I developed new models for count-regression-model-based (CRM-based) causal effect estimation in which the value for the outcome of interest is restricted to the non-negative integers. I implement first-order two-stage residual inclusion (FO-2SRI) methods, in the …