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Topics in imbalanced data classification : AdaBoost and Bayesian relevance vector machine
(University of Missouri--Columbia, 2020)
This research has three parts addressing classification, especially the imbalanced data problem, which is one of the most popular and essential issues in the domain of classification. The first part is to study the Adaptive ...
Modeling chronic wasting disease using Gaussian Process Boosting
(University of Missouri--Columbia, 2023)
Chronic Wasting Disease (CWD) is a fatal neurological condition that affects cervids (white tail deer, elk, mule deer, etc.). Veterinary epidemiologists at the state and federal level are interested in methods to accurately ...
Methodologies for low-rank analysis and regionalization for multi-scale spatial datasets
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 5/1/2024] This dissertation comprises three chapters that focus on developing low-rank modeling and spatial aggregation techniques to overcome the computational and storage challenges associated with ...
Average treatment effect evaluation with time-to-event data in randomized clinical trials and observational studies
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 5/1/2024] The average treatment effect (ATE) is defined as the difference in the expected outcome between individuals receiving the treatment and those not receiving it. As a measure of the impact of a ...
Regression analysis of longitudinal covariates with censored and longitudinal outcome
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Longitudinal data contain repeated measurements of variables on the same experimental subject. It is often of interest to analyze the relationship ...
Statistical methods to deflect allele specific expression, alterations of allele specific expression and differential expression
(University of Missouri--Columbia, 2021)
The advent of next-generation sequencing (NGS) technology has facilitated the recent development of RNA sequencing (RNA-seq), which is a novel mapping and quantifying method for transcriptomes. By RNA-seq, one can measure ...
Regression analysis of correlated interval-censored failure time data with a cured subgroup
(University of Missouri--Columbia, 2021)
Interval-censored failure time data commonly occur in many periodic follow-up studies such as epidemiological experiments, medical studies and clinical trials. By intervalcensored data, we usually mean that one cannot ...
Semiparametric analysis of time-to-event data and longitudinal data
(University of Missouri--Columbia, 2021)
Interval-censored failure time data are commonly observed in demographical, epi-demiological, financial, medical, and sociological studies. It is well-known that the proportional hazards model is one of the most used ...
Bayesian smoothing spline with dependency models
(University of Missouri--Columbia, 2021)
The smoothing spline model is widely used for fitting a smooth curve because of its flexibility and smoothing properties. Our study is motivated by estimating the long-term trend of the U.S. unemployment level. In this ...
Nonstationary Bayesian time series models with time-varying parameters and regime-switching
(University of Missouri--Columbia, 2021)
Nonstationary time series data exist in various scientic disciplines, including environmental science, biology, signal processing, econometrics, among others. Many Bayesian models have been developed to handle nonstationary ...
Bayesian model averaging for mathematics achievement growth rate trends
(University of Missouri--Columbia, 2022)
In this study, we investigated the use of Bayesian model averaging (BMA) for latent growth curve models. We used the Trends in International Mathematics and Science Study (TIMSS) to predict growth rates in 8th-grade students' ...
Scalable Bayesian nonparametric learning for biomedical big data
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recent advances in array-based and next-generation sequencing (NGS) technologies have revolutionized biomedical research, especially in cancer. The ...