Statistics electronic theses and dissertations (MU)
The items in this collection are the theses and dissertations written by students of the Department of Statistics. Some items may be viewed only by members of the University of Missouri System and/or University of MissouriColumbia. Click on one of the browse buttons above for a complete listing of the works.
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Recent Submissions

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)Statistics 
Semiparametric regression analysis of intervalcensored failure time data
(University of MissouriColumbia, 2014)Statistics 
THE FORMAL DEFINITION OF REFERENCE PRIORS UNDER A GENERAL CLASS OF DIVERGENCE
(University of MissouriColumbia, 2014)Statistics 
OPTIMAL EXPERIMENTAL DESIGN UNDER A NEW MULTIVARIATE WEIBULL REGRESSION FUNCTION
(University of MissouriColumbia, 2014)Statistics 
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 ... 
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 ... 
Adaptive optimal designs for dosefinding studies and an adaptive multivariate CUSUM control chart
(University of MissouriColumbia, 2013)There are many areas where optimal designs are applied to, for example, the development of a new drug, where a conventional dose finding study involves learning about the doseresponse curve in order to bring forward right ... 
Statistical analysis of lengthbiased and rightcensored data
(University of MissouriColumbia, 2013)Biased sampling arises when the observations are not randomly selected from the target population. When the sampling probability is proportional to the underlying outcome of interest, this is known as lengthbiased sampling. ... 
Two stage adaptive optimal design with applications to dosefinding clinical trials
(University of MissouriColumbia, 2013)In adaptive optimal designs, each stage uses an estimate of the locally optimal design derived using cumulative data from all prior stages. This dependency on prior stages a ffects the properties of maximum likelihood ... 
Hierarchical modeling of nonlinear multivariate spatiotemporal dynamical systems in the presence of uncertainty
(University of MissouriColumbia, 2012)Dynamic spatiotemporal models are statistical models that specify the joint distribution of a spatiotemporal process as the product of a series of conditional models whereby the current value of the process is conditioned ... 
Bayesian fMRI data analysis and Bayesian optimal design
(University of MissouriColumbia, 2012)The present dissertation consists of the work done on two projects. As part of the first project, we develop methodology for Bayesian hierarchical multisubject multiscale analysis of functional magnetic resonance imaging ... 
Regression analysis of clustered intervalcensored failure time data
(University of MissouriColumbia, 2012)Clustered failure time data occur when the failure times of interest are clustered into small groups, while interval censoring occurs when the event of interest cannot be observed directly and is only known to have occurred ... 
Objective Bayesian inference for stressstrength models and Bayesian ANOVA
(University of MissouriColumbia, 2012)First of all, for estimating the reliabilities in Weibull stressstrength models, some matching priors are derived based on a modi ed pro le likelihood. Simulation studies show that these matching priors perform well with ... 
Estimating population size with objective Bayesian methods
(University of MissouriColumbia, 2012)Bayesian inference of discrete parameter, including population size, is sensitive to the choice of priors. In this dissertation I will develop objective priors for several population size parameters appeared in different ... 
Bayesian analysis of spatial and survival models with applications of computation techniques
(University of MissouriColumbia, 2012)This dissertation discusses the methodologies of applying Bayesian hierarchical models to different data with geographical characteristics or with rightcensored failure time. A conditional autoregressive (CAR) prior is ... 
Bayesian methods on selected topics
(University of MissouriColumbia, 2012)Bayesian methods are widely adopted nowadays in statistical analysis. It is especially useful for the statistical inference of complex models or hierarchical models, for which the frequentist methods are usually difficult ...