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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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 Bayesian ... -
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 ... -
Bayesian non-parametric methods for benefit-risk assessment and massive multiple-domain data
(University of Missouri--Columbia, 2019)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The development of systematic and structured approaches to assess benefit-risk of medical products is a major challenge for regulatory decision makers. ... -
Variable selection for interval-censored failure time data
(University of Missouri--Columbia, 2019)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Variable selection is a commonly asked question and various traditional variable selec- tion methods have been developed, including forward, backward ... -
Modeling gibbs point processes through basic function decompositions
(University of Missouri--Columbia, 2019)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] We consider non-homogeneous pairwise interaction point process models, where the global and local effect functions are modeled using basis function ... -
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
(University of Missouri--Columbia, 2019)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] 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. ... -
Reference analysis of non-regular models and nonparametric Bayes modeling of large data
(University of Missouri--Columbia, 2019)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Bayesian analysis is a principled approach, which makes inference about the parameter, by combining the information gained from the data and the prior ... -
Statistical-based dynamic machine learning models for nonlinear spatio-temporal processes
(University of Missouri--Columbia, 2018)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] 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 ... -
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)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recent advances in array-based and next-generation sequencing (NGS) technologies have revolutionized biomedical research, especially in cancer. The ... -
Regression analysis of longitudinal covariates with censored and longitudinal outcome
(University of Missouri--Columbia, 2018)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Longitudinal data contain repeated measurements of variables on the same experimental subject. It is often of interest to analyze the relationship ... -
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. ...