Browsing Theses (MU) by Thesis Department "Statistics (MU)"
Now showing items 17 of 7

A Bayesian classification framework with label corrections
(University of MissouriColumbia, 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 ... 
Bayesian lasso for random intercept factor model
(University of MissouriColumbia, 2013)Structural Equation Models (SEM) are often used in psychological research. In many studies, determining the number of variables is di fficult because maximum likelihood estimates are empirically underidenti fied when more ... 
Empirical likelihood approach estimation of structural equation models
(University of MissouriColumbia, 2007)This thesis provides a preliminary investigation of empirical likelihood approach estimation of structural equation models. An auxiliary variable approach built on general estimating equation methods in the EL settings is ... 
Estimates of school productivity and implications for policy
(University of MissouriColumbia, 2007)School productivity was not perfectly estimated because of the sampling error and the measurement error. The traditional Ordinary Least Square (OLS) leaves the estimation of school productivity questionable. Moreover, ... 
A mixed model for variance of successive difference of stationary time series: modeling temporal instability in intensive longitudinal data
(University of MissouriColumbia, 2008)Temporal instability of a stochastic process has been of interest in many areas of behavioral and social science. Recent development in data collection techniques in behavioral and health sciences, such as Ecological ... 
Statistical image analysis of photoactivated localization microscopy
(University of MissouriColumbia, 2013)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Fluorescent microscopy is a traditional way of localizing the biological molecules in the living cells. However, the diffraction limit makes it difficult ... 
Two sample inference for high dimensional mean with application to gene expression data
(University of MissouriColumbia, 2015)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Motivated by the gene expression analysis from biological science, we consider the two sample problem, where the number of variables is much larger ...