RSS 1.0Department of Statistics (MU)
Statisticians are in demand in education, medicine, government, business and industry as well as in the biological, social and physical sciences. The department has prided itself training students to meet this need since its creation in 1963. Our faculty are known for both cutting edge methodological and collaborative research and for outstanding teaching. Faculty members are currently investigating statistical problems in the fields of ecology, genetics, economics, meteorology, wildlife management, epidemiology, AIDS research, geophysics, and climatology. We maintain strong ties with other departments and research groups, especially economics, psychology, atmospheric science and the Missouri Department of Conservation.
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Bayesian and machine learning models for dependent data with applications to official statistics and survey methodology
(University of Missouri--Columbia, 2023)[EMBARGOED UNTIL 8/1/2024] Small Area estimation has garnered much interest in recent times by both private entities as well government agencies as means of public policy guidance, formulating programs for regional and ... -
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 ... -
Modeling spatio-temporal data using a Bayesian probabilistic cellular automata framework
(University of Missouri--Columbia, 2023)Regularly gridded, or cellular, discrete-valued spatio-temporal data are common in many application areas. Such data can be considered from many perspectives, including deterministic or stochastic cellular automata, where ... -
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 ... -
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 ... -
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 ...