RSS 1.0Statistics electronic theses and dissertations (MU)
The items in this collection are the theses and dissertations written by students of the Department of Statistics. Some items may be viewed only by members of the University of Missouri System and/or University of Missouri-Columbia. Click on one of the browse buttons above for a complete listing of the works.
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Recent Submissions
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Variable selection for interval-censored and functional survival data
(University of Missouri--Columbia, 2022)[EMBARGOED UNTIL 12/1/2023] Interval-censored data are a type of failure time data that is only known to belong to a time interval but cannot be observed precisely. Note that interval-censoring is often encountered in ... -
Bayesian cusp regression and linear mixed model
(University of Missouri--Columbia, 2022)First of all, we introduce the Bayesian mixture way of solving the Cusp Catastrophe model, which is designed to deal with piece-wise continuous outcomes. Simulation and real data analysis show that the new method beats the ... -
Modeling state duration and emission dependence in hidden Markov and hidden semi-Markov models
(University of Missouri--Columbia, 2022)Hidden Markov models (HMM) are composed of a latent state sequence and an observation sequence conditionally independent on the states, which follows an emission distribution. Hidden semi-Markov models (HSMM) extend the ... -
A Bayesian approach to data-driven discovery of nonlinear dynamic equations
(University of Missouri--Columbia, 2022)Dynamic equations parameterized by differential equations are used to represent a variety of real-world processes. The equations used to describe these processes are generally derived based on physical principles and a ... -
A class of weakly informative prior on multinomial logistic regression with separated data
(University of Missouri--Columbia, 2021)Complete separation in logistic regression, sometimes referred to as perfect prediction, occurs when the outcome variable completely separates predictor variables. The likelihood function is monotonically increasing on the ... -
Bayesian model averaging for mathematics achievement growth rate trends
(University of Missouri--Columbia, 2022)[EMBARGOED UNTIL 6/1/2023] 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 ... -
Data combining using mixtures of g-priors with application on county-level female breast cancer prevalence
(University of Missouri--Columbia, 2022)[EMBARGOED UNTIL 5/31/2023] As more and more data are available, data synthesis has become an indispensable task for researchers. From a Bayesian perspective, this dissertation includes three related projects and aims at ... -
Variable selection and causal treatment effect estimation based on interval-censored failure time data
(University of Missouri--Columbia, 2022)[EMBARGOED UNTIL 5/31/2023] Variable selection has been discussed under many contexts and especially a great deal of literature has been established in the failure time context with constant coefficients. However, the ... -
Bayesian unit-level modeling of non-Gaussian survey data under informative sampling with application to small area estimation
(University of Missouri--Columbia, 2021)Unit-level models are an alternative to the traditional area-level models used in small area estimation, characterized by the direct modeling of survey responses rather than aggregated direct estimates. These unit-level ... -
Dynamic spatial-temporal point process models via conditioning
(University of Missouri--Columbia, 2021)We propose and investigate dynamic spatial-temporal point process models for independent and interacting events. The models for independent events are dynamic spatial-temporal Poisson point process (DSTPPP) model that ... -
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 ... -
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 ... -
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 ... -
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 ... -
Dynamic analysis of complex panel count data
(University of Missouri--Columbia, 2021)Panel count data occur in many fields including clinical, demographical and industrial studies and an extensive literature has been established for their regression analysis. However, most of the existing methods apply ... -
Alternative learning strategies for spatio-temporal processes of complex animal behavior
(University of Missouri--Columbia, 2020)The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven ... -
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 ... -
Decision theory and sampling algorithms for spatial and spatio-temporal point processes
(University of Missouri--Columbia, 2019)In this work, we first present a flexible hierarchical Bayesian model and develop a comprehensive Bayesian decision theoretic framework for point process theory. Then, we provide a comparative study of the approximate ... -
Semiparametric analysis of complex longitudinal data
(University of Missouri--Columbia, 2020)Event history data consist of the longitudinal records of event occurrence times. Recurrent event data and panel count data are two common types of event history data that occur in many areas, such as medical studies and ...