Search
Now showing items 21-40 of 112
Random set models for growth with applications to nowcasting
(University of Missouri--Columbia, 2013)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] 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 ...
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)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The Booth lab at the University of Missouri has selectively-bred Wistar rats for low (LVR) and high (HVR) voluntary running behavior as a model for ...
The nonparametric analysis of interval-censored failure time data
(University of Missouri--Columbia, 2013)
By interval-censored 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 interval-censored observation for a ...
Semiparametric and nonparametric methods for the analysis of panel count data
(University of Missouri--Columbia, 2013)
Panel count data are one type of event-history data concerning recurrent events. Ideally for an event-history study, subjects should be monitored continuously, so for the events that may happen recurrently over time, the ...
Regression analysis of clustered interval-censored failure time data
(University of Missouri--Columbia, 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 ...
Partially informative normal and partial spline models
(University of Missouri--Columbia, 2015)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] There is a well-known Bayesian interpretation of function estimation by spline smoothing using a limit of proper normal priors. This limiting prior ...
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 ...
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 ...
Variable selection for interval-censored and functional survival data
(University of Missouri--Columbia, 2022)
Interval-censored data are a type of failure time data that is only known to belong to a time interval but cannot be observed precisely. Note that interval-censoring is often encountered in medical or health studies with ...
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 ...
Bayesian semiparametric spatial and joint spatio-temporal modeling
(University of Missouri--Columbia, 2006)
Over the past decades a great deal of effort has been expended in the collection and compilation of high quality data on cancer incidence and mortality in the United States. These data have largely been used in the creation ...
Optimal designs for dose-finding in contingent response models
(University of Missouri--Columbia, 2004)
We study D- and c-optimal designs for dose-finding with opposing failure functions. In particular, we study the contingent response models of Li, Durham and Flournoy (1995). In the contingent response model, there are two ...
Bayesian spatial data analysis with application to the Missouri Ozark forest ecosystem project
(University of Missouri--Columbia, 2006)
The first part studies the problem of estimating the covariance matrix in a star-shaped model with missing data. By introducing a class of priors based on a type of Cholesky decomposition of the precision matrix, we then ...
Nonparametric and semiparametric methods for interval-censored failure time data
(University of Missouri--Columbia, 2006)
Interval-censored failure time data commonly arise in follow-up studies such as clinical trials and epidemiology studies. By interval-censored data, we mean that the failure time of interest is not completely observed. ...
Hierarchical spatio-temporal models for ecological processes
(University of Missouri--Columbia, 2006)
Ecosystems are composed of phenomena that propagate in time and space. Often, ecological processes underlying such phenomena are studied separably in various subdisciplines, while larger scale, interlinking mechanisms are ...
Statistical analysis of multivariate interval-censored failure time data
(University of Missouri--Columbia, 2006)
Interval-censored failure time data commonly arise in clinical trials and medical studies. In such studies, the failure time of interest is often not exactly observed, but known to fall within some interval. For multivariate ...
Bayesian analysis of multivariate stochastic volatility and dynamic models
(University of Missouri--Columbia, 2006)
We consider a multivariate regression model with time varying volatilities in the error term. The time varying volatility for each component of the error is of unknown nature, may be deterministic or stochastic. We propose ...
Alternative learning strategies for spatio-temporal processes of complex animal behavior
(University of Missouri--Columbia, 2020)
The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven ...
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 ...