Department 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 hierarchical models for estimating nest survival
(University of MissouriColumbia, 2015) 
Two sample inference for high dimensional mean with application to gene expression data
(University of MissouriColumbia, 2015) 
Bayesian analysis of capturerecapture model and diagnostic test in clinical trials
(University of MissouriColumbia, 2014)Capturerecapture models have been widely used to estimate the size of a target wildlife population. There are three major sources of variations that can affect capture probabilities: time (i.e., capture probabilities vary ... 
Bayesian analysis for detecting differentially expressed genes from RNAseq data
(University of MissouriColumbia, 2014)This dissertation introduces hmmSeq, a modelbased hierarchical Bayesian technique for detecting differentially expressed genes from RNAseq data. Our novel hmmSeq methodology uses hidden Markov models to account for ... 
Statistical Analysis of Bivariate IntervalCensored Failure Time Data
(University of MissouriColumbia, 2015)Intervalcensored failure time data arise when the failure time of interest in a survival study is not exactly observed but known only to fall within some interval. One area that often produces such data is medical studies ... 
Model Evaluation and Variable Selection for IntervalCensored Data
(University of MissouriColumbia, 2015)Survival analysis is a popular area of statistics dealing with timetoevent data. A special characteristic of survival data is the presence of censoring. Censoring occurs when the survival time is only partially known. ... 
Random Set Models For Growth With Applications To Nowcasting
(University of MissouriColumbia, 2013)We develop models to capture the growth or evolution of objects over time as well as provide forecasts to describe the object in future states utilizing information from the current state. For this purpose, we propose ... 
Flexible Bayesian Hierarchical Models for DiscreteValued SpatioTemporal Data
(University of MissouriColumbia, 2014) 
Equivalence test of high dimensional microarray data
(University of MissouriColumbia, 2014) 
Spatiotemporal models with timevarying spatial model error for environmental processes
(University of MissouriColumbia, 2013)Environmental processes exhibit uncertainty in the spatial and temporal domains. Often, mechanistic forecast models, such as weather forecasting systems, may not necessarily match the observed data, resulting in the need ... 
Bayesian nonlinear methods for survival analysis and structural equation models
([University of MissouriColumbia], 2014)High dimensional data are more common nowadays, because the collection of such data becomes larger and more complex due to the technology advance of the computer science, biology, etc. The analysis of high dimensional data ... 
Semiparametric regression analysis of intervalcensored failure time data
([University of MissouriColumbia], 2014)By intervalcensored data, we mean that the failure time of interest is known only to lie within an interval instead of being observed exactly. Many clinical trials and longitudinal studies may generate intervalcensored ... 
THE FORMAL DEFINITION OF REFERENCE PRIORS UNDER A GENERAL CLASS OF DIVERGENCE
([University of MissouriColumbia], 2014)Bayesian analysis is widely used recently in both theory and application of statistics. The choice of priors plays a key role in any Bayesian analysis. There are two types of priors: subjective priors and objective priors. ... 
OPTIMAL EXPERIMENTAL DESIGN UNDER A NEW MULTIVARIATE WEIBULL REGRESSION FUNCTION
([University of MissouriColumbia], 2014)In the manufacturing industry, it may be important to study the relationship between machine component failures under stress. Examples include failures such as integrated circuits and memory chips in electronic merchandise ... 
Hierarchical nonlinear, multivariate, and spatiallydependent timefrequency functional methods
(University of MissouriColumbia, 2013)Notions of time and frequency are important constituents of most scientific inquiries, providing complimentary information. In an era of “big data,” methodology for analyzing functional and/or image data is increasingly ... 
Semiparametric and nonparametric methods for the analysis of panel count data
(University of MissouriColumbia, 2013)Panel count data are one type of eventhistory data concerning recurrent events. Ideally for an eventhistory study, subjects should be monitored continuously, so for the events that may happen recurrently over time, the ... 
The nonparametric analysis of intervalcensored failure time data
(University of MissouriColumbia, 2013)By intervalcensored failure time data, we mean that the failure time of interest is observed to belong to some windows or intervals, instead of being known exactly. One would get an intervalcensored observation for a ... 
Statistical image analysis of photoactivated localization microscopy
(University of MissouriColumbia, 2013)Fluorescent microscopy is a traditional way of localizing the biological molecules in the living cells. However, the diffraction limit makes it difficult to resolve any molecules whose distance are less than 250 nm. To ... 
Bayesian lasso for random intercept factor model
(University of MissouriColumbia, 2013)Structural Equation Models (SEM) are often used in psychological research. In many studies, determining the number of variables is di fficult because maximum likelihood estimates are empirically underidenti fied when more ... 
Design and analysis of a new bounded loglinear regression model
(University of MissouriColumbia, 2013)This dissertation introduces a new regression model in which the response variable is bounded by two unknown parameters. A special case is a bounded alternative to the four parameter logistic model which is also called the ...