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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Statistical analysis of clustered or multivariate interval-censored failure time data
(University of Missouri--Columbia, 2018)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Interval-censored failure time data are a type of the failure time data that often occur in clinical trials with periodic follow-ups among others. In ... -
Point processes on the complex plane with applications
(2019)A point process is a random collection of points from a certain space, and point process models are widely used in areas dealing with spatial data. However, studies of point process theory in the past only focused on ... -
Reference analysis of non-regular models and nonparametric Bayes modeling of large data
(2019)Bayesian analysis is a principled approach, which makes inference about the parameter, by combining the information gained from the data and the prior belief about the parameter. There's no convergence on the choice of ... -
Statistical-based dynamic machine learning models for nonlinear spatio-temporal processes
(University of Missouri--Columbia, 2018)One of the most vital aspects of any spatio-temporal model is characterizing the dynamics of the process. In both a spatio-temporal forecasting and data assimilation setting, the dynamical process model often determines ... -
Full Bayesian models for paired RNA-seq data and Bayesian equivalence test
(University of Missouri--Columbia, 2018)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] "In my doctorate research, I have developed Bayesian models to analyze the paired RNAseq data for different types of design. The developed methods are ... -
Scalable Bayesian nonparametric learning for biomedical big data
(University of Missouri--Columbia, 2018)Recent advances in array-based and next-generation sequencing (NGS) technologies have revolutionized biomedical research, especially in cancer. The analysis of the datasets compiled from these technologies, usually referred ... -
Regression analysis of longitudinal covariates with censored and longitudinal outcome
(University of Missouri--Columbia, 2018)Longitudinal data contain repeated measurements of variables on the same experimental subject. It is often of interest to analyze the relationship between these variables. Typically, there is one or several longitudinal ... -
Objective Bayesian analysis of the 2 x 2 contingency table and the negative binomial distribution
(University of Missouri--Columbia, 2018)In Bayesian analysis, the “objective” Bayesian approach seeks to select a prior distribution not by using (often subjective) scientific belief or by mathematical convenience, but rather by deriving it under a pre-specified ... -
Regression analysis of interval-censored failure time data with non proportional hazards models
(University of Missouri--Columbia, 2018)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Interval-censored failure time data arises when the failure time of interest is known only to lie within an interval or window instead of being observed ... -
Bayesian hierarchical modeling of colorectal and breast cancer data in Missouri
(University of Missouri--Columbia, 2018)Data on cancer in the United States is collected through cancer registries. The Missouri Cancer Registry and Research Center (MCR-ARC) maintains a statewide cancer surveillance system and participate in research in support ... -
A Bayesian classification framework with label corrections
(University of Missouri--Columbia, 2014)The use of unlabeled data is very important for regression and classification analysis in many cases. However, the data may have an extra layer of complexity with some wrongly labelled data points. The traditional ... -
Semiparametric methods for regression analysis of panel count data and mixed panel count data
(University of Missouri--Columbia, 2017)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recurrent event data and panel count data are two common types of data that have been studied extensively in event history studies in literature. By ... -
Bayesian partition model for identifying hypo- and hyper- methylation
(University of Missouri--Columbia, 2017)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation introduces MethyBayes, a full Bayesian partition model for identifying hypo- and hypermethylated loci. The main interest of study on ... -
Some topics in multi-regional clinical trials and meta-analysis using Bayesian models
(University of Missouri--Columbia, 2017)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The dissertation consists of two distinct research topics. One is about sample size determination in Multi-Regional Clinical Trials (MRCTs), the other ... -
Functional data analysis : children's mathematical development
(University of Missouri--Columbia, 2016)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Bailey et al. (2014) suggested that children's mathematical development is related more to trait characteristics than to prior mathematical development. ... -
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)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Failure time data arise in many fields including biomedical studies and industrial life testing. Right-censored failure time data are often observed ... -
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)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] There is a well-known Bayesian interpretation of function estimation by spline smoothing using a limit of proper normal priors. This limiting prior has ... -
Bayesian smoothing spline models and their application in estimating yield curves
(University of Missouri--Columbia, 2015)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] 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 ...