Physical Sciences and Mathematics Commons^™

Impact Of Agreeableness On Virtual Team Performance Through Team Identification And Shared Mental Models, Alexandria Brown

Graduate Research Theses & Dissertations

Virtual teams help organizations efficiently utilize their employees for a task without the requirement of co-location. The literature on team performance suggests that teamwork is integral to a team’s success; however, in virtual teams this is often a challenge. Certain personality characteristics on virtual teams may be particularly important to the development of effective teamwork. An under-investigated factor is the role agreeableness in virtual team processes and how it affects the overall team performance. The main research question of this study is how the degree of agreeableness on a virtual team affects the overall team performance through predicted associations with …

Maximum Likelihood Estimation For A Heavy-Tailed Mixture Distribution, Philippe Kponbogan Dovoedo

Graduate Research Theses & Dissertations

In an increasingly connected global environment, “high-impact, low-probability" (HILP)

events can have devastating consequences and result in large insurance losses with a heavy-

tailed distribution. Examples of such events include Hurricane Katrina, the Deepwater

Horizon oil disaster and the Japanese nuclear crisis and tsunami. According to the 2012

Blackett Review of HILP Risks from the UK Government Office for Science, the

identification of low-probability risks, and the subsequent development of mitigation plans,

is complicated by their rare or conjectural nature, and their potential for causing impacts

beyond everyday experience. Extremal mixture models and more generally extreme value

analysis help assess …

Simulating And Modelling Opinion Dynamics, Jennifer Heermance

Graduate Research Theses & Dissertations

The foundation of social media is conversation. Social media allows people to share ideas and opinions, as well as discuss those opinions. A point of intrigue for many social scientists is how those opinions change through interaction with others. What influences someone’s opinion? When is a person willing to adapt their opinion, and when does it remain the same? Is it possible to measure these opinion dynamics? Our overall goal is to develop a more comprehensive model for opinion dynamics. The first step of this process is to simulate data that can then be analyzed and used to develop a …

Assessing The Performance And Merit Of The Random Survival Forest And Cox Models On A Pancreatic Cancer Data Set, Carl Edward Mueller

Graduate Research Theses & Dissertations

Random Survival Forest (RSF) is one of the most powerful and easily applied machine learning models for survival data. RSF sacrifices some of the interpretability of the decision trees used to grow the forest in order to significantly reduce the bias and variance of the basic classification and regression tree (CART) paradigm. The lessened interpretability and higher computational intensity of RSF means that it may not always be the preferred method, even in settings where black-box methods are readily used. By contrast, the Cox Proportional Hazards (PH) model is incredibly flexible, resistant to overfitting, and transparently estimable. The tradeoff for …

Bayesian Lasso Survival Analysis, Justin P. Neely

Graduate Research Theses & Dissertations

This thesis examines the use of Bayesian LASSO regression for survival data to estimate the survival function and to select significant covariates simultaneously. We consider survival times of patients with adenocarcinoma lung cancer. The survival and genetic data are available in the Cancer Genome Atlas (TCGA) Research Network. As a pilot study, within chromosome 5, we apply Bayesian LASSO regression to explore genetic markers that may help to identify crucial genes to determine survival times of patients. Using Gibbs sampling we can obtain Markov Chain Monte Carlo samples for regression coefficients and model variance as well as LASSO penalty from …

Bivariate Cure Rate Model Using Copula Functions In Presence Of Censored Data And Covariates, Jie Huang

Graduate Research Theses & Dissertations

Bivariate survival cure rate models extend the understanding of time-to-event data by allowing for the formulation of more accurate and informative conclusion. These conclusions are obtainable from an analysis that accounts for a cured fraction of the population and dependence between paired units. We propose a mixture cure rate model where a correlation coefficient is used for the association between bivariate cure rate fractions and a new generalized Farlie Gumbel Morgenstern (FGM) copula function is applied to model the de-

pendence structure of bivariate survival times. Covariate effects are incorporated into two components of our model, cure rate fractions and …

Applications Of Bayesian Functional Data Analysis, Hao Shen

Graduate Research Theses & Dissertations

Functional Data Analysis (FDA) is a set of statistical methods that can deal with the data which represent curves or functions. In this dissertation, we consider two extensions of FDA to two types of data, circadian data and multidimensional data. The first part of the dissertation is concerned with the analysis of circadian data. We estimate circadian functions by using Bayesian smoothing splines under the generalized linear model, and extract two measures from each estimated function, magnitude and roughness. Based on extracted measures, we cluster individual functions into normal group and abnormal group by utilizing a density based clustering method. …

Exploring A Bayesian Analysis Of Opinion Dynamics Using The Approximate Bayesian Computation Method, Jessica L. Bishop

Graduate Research Theses & Dissertations

Social media has created a whole new framework in the way we understand ones expression of opinion, and how ones' opinion can influence others. Models of opinion dynamics, such as a probabilistic modeling framework of opinion dynamics over time are given by Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, and Manuel Gomez Rodriguez in ``Learning and Forecasting Opinion Dynamics in Social Networks." In this paper, we will continue to explore their models, now coming from a Bayesian statistical standpoint, specifically looking at the Approximate Bayesian Computation (ABC) method for the computation of better estimations for the data. We will …

Evaluation Of Epilepsy Surgery Using Bayesian Multinomial Regression, Kacy Danielle Kane

Graduate Research Theses & Dissertations

We are exploring the effectiveness of brain surgeries that are supposed to eliminate or reduce the frequency of seizures in young Epilepsy patients. The long-term effectiveness of brain surgeries is evaluated by ordinal categories and brings longitudinal categorical responses.

Using a Bayesian multinomial regression model we examine the responses by the lobe of brain and other covariates as well as time. To overcome computational difficulties we utilize latent variables for multinomial responses and compare the results with frequentists methods.

Bayesian Functional Data Analysis Over Dependent Regions And Its Application For Identification Of Differentially Methylated Regions, Suvo Chatterjee

Graduate Research Theses & Dissertations

Bayesian functional data analysis (BFDA) provides flexible statistical inferences under harsh circumstances such as a large volume of data, considerable measurement errors and missing observations. Considering a sequence of segments and functional data analysis on each segment, where neighboring segments can be dependent, demanding computation is indispensable and analysis is sometimes infeasible for large number of segments. We consider a utilization of BFDA to identify differentially methylated regions (DMRs). Out of numerous existing methodologies to detect DMRs, there still does not exist a standard approach to identify DMRs especially under the assumption of dependency among genomic regions. In this dissertation, …