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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Functional data analysis : children's mathematical development
(University of Missouri--Columbia, 2016)Bailey et al. (2014) suggested that children's mathematical development is related more to trait characteristics than to prior mathematical development. However, their study analysis was designed to examine trait and state ... -
Bayesian analysis of fMRI data and RNA-Seq Time Course experiment data
(University of Missouri--Columbia, 2015)The present dissertation contains two parts. In the first part, we develop a new Bayesian analysis of functional MRI data. We propose a novel triple gamma Hemodynamic Response Function (HRF) including the component to ... -
Semiparametric analysis of failure time data with complex structures
(University of Missouri--Columbia, 2016)Failure time data arise in many fields including biomedical studies and industrial life testing. Right-censored failure time data are often observed from a cohort of prevalent cases that are subject to length-biased sampling, ... -
Nonlocal priors for Bayesian variable selection in generalized linear models and generalized linear mixed models and their applications in biology data
(University of Missouri--Columbia, 2016)A crucial problem in building a generalized linear model (GLM) or a generalized linear mixed model (GLMM) is to identify which subset of predictors should be included into the model. Hence, the main thrust of this dissertation ... -
Partially informative normal and partial spline models
(University of Missouri--Columbia, 2015)There is a well-known Bayesian interpretation of function estimation by spline smoothing using a limit of proper normal priors. This limiting prior has the same form with Partially Informative Normal (PIN), which was ... -
Bayesian smoothing spline models and their application in estimating yield curves
(University of Missouri--Columbia, 2015)The term structure of interest rates, also called the yield curve, is the series of interest rates ordered by term to maturity at a given time. The smoothing spline as a nonparametric regression method has been used widely ... -
Bayesian hierarchical models for estimating nest survival
(University of Missouri--Columbia, 2015)Nest survival rate is a critical value in avian study to evaluate the landbirds populations. The widely used likelihood-based logistic regression model was evaluated in the first part of the dissertation. In this part, we ... -
A ballooned beta-logistic model
(University of Missouri--Columbia, 2015)The beta distribution is a simple and flexible model in which responses are naturally confined to the finite interval (0,1). Its parameters can be related to covariates such as dose and gender through a regression model. ... -
Bayesian hierarchical models for estimating nest survival
(University of Missouri--Columbia, 2015)Nest survival rate is a critical value in avian study to evaluate the landbirds populations. The widely used likelihood-based logistic regression model was evaluated in the first part of the dissertation. In this part, we ... -
Two sample inference for high dimensional mean with application to gene expression data
(University of Missouri--Columbia, 2015) -
Bayesian analysis of capture-recapture model and diagnostic test in clinical trials
(University of Missouri--Columbia, 2014)Capture-recapture 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 RNA-seq data
(University of Missouri--Columbia, 2014)This dissertation introduces hmmSeq, a model-based hierarchical Bayesian technique for detecting differentially expressed genes from RNA-seq data. Our novel hmmSeq methodology uses hidden Markov models to account for ... -
Statistical Analysis of Bivariate Interval-Censored Failure Time Data
(University of Missouri--Columbia, 2015)Interval-censored 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 Interval-Censored Data
(University of Missouri--Columbia, 2015)Survival analysis is a popular area of statistics dealing with time-to-event 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 Missouri--Columbia, 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 Discrete-Valued Spatio-Temporal Data
(University of Missouri--Columbia, 2014) -
Equivalence test of high dimensional microarray data
(University of Missouri--Columbia, 2014) -
Spatio-temporal models with time-varying spatial model error for environmental processes
(University of Missouri--Columbia, 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 non-linear methods for survival analysis and structural equation models
([University of Missouri--Columbia], 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 ... -
Semi-parametric regression analysis of interval-censored failure time data
([University of Missouri--Columbia], 2014)By interval-censored 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 interval-censored ...