Browsing Statistics electronic theses and dissertations (MU) by Thesis Semester "2019 Spring"
Now showing items 1-4 of 4
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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. ... -
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 ... -
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 ... -
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 selection methods have been developed, including forward, backward and ...