Statistics and Probability Commons^™

Examining Covid-19 Vaccine Hesitancy Among The Nevada African American Population Using The Social-Ecological Model, Katelyn Faulk

UNLV Theses, Dissertations, Professional Papers, and Capstones

In Nevada, COVID-19 vaccines have been widely available to the general population since March 2021; however, even with the wide availability of these vaccines only 40% of the African American population in Nevada has been fully vaccinated against COVID-19 as of May 2023. This is problematic as it has been shown that the African American population is disproportionately affected by COVID-19 with higher rates of cases, hospitalizations, and deaths when compared to other races or ethnicities. Through the literature, it has also been well documented that African Americans may experience hesitancy toward these vaccinations for a multitude of reasons including …

Synergy And Antagonism In Log-Linear Models, Md Nahid Hasan

UNLV Theses, Dissertations, Professional Papers, and Capstones

Synergy and antagonism have been extensively studied in the context of the statistical analysis of drug combinations given to treat a disease. “Synergy” (“antagonism”, resp.) in a drug combination occurs when the desirable effect of two drugs given together for treating a disease is greater than (less than, resp.) the effect of each drug given separately.

In this dissertation, however, we study “synergy” and “antagonism” in log-linear models. We also give a thorough review and extend several epidemiological definitions of synergy and antagonism for categorical data, and we connect these definitions to the definitions of synergy and antagonism in log-linear …

Benchmarking And Practical Evaluation Of Machine And Statistical Learning Methods In Credit Scoring: A Method Selection Perspective, Gwen Verbeck

UNLV Theses, Dissertations, Professional Papers, and Capstones

Predictive models are important tools used in all scientific fields. Machine learning (ML) algorithms and statistical models are widely used for decision-making because of their capability to tackle intricate and unique problems. In domains where data are high-dimensional and contain irrelevant and redundant features, ML algorithms are known to have superior performance over traditional (statistical) learning methods. However, researchers and analysts are often faced with a myriad of techniques to choose from, with no clear consensus on which will perform best for their specific task. Considering resource limitations, exhaustive exploration of all available methods is impractical and often fails to …

Development Of A Metapgs For Accurate Prediction Of Osteoporotic Fracture, Xiangxue Xiao

UNLV Theses, Dissertations, Professional Papers, and Capstones

Introduction: Early identification of individuals at high-risk for osteoporotic fractures who may benefit from preventive intervention is essential. However, the predictive accuracy of the currently used fracture risk assessment tool remains suboptimal. The first aim of this research is to construct genome-wide polygenic scores for the femoral neck (PGS_FNBMD_idpred) and total body BMD (PGS_TBBMD_idpred) and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures. The second aim is to validate the predictive performance of two previously established PGSs (PGS_FNBMD_idpred and PGS_TBBMD_idpred) in an external cohort …

A Generalized Family Of Exponentiated Composite Distributions With Applications To Insurance And Survival Data, Bowen Liu

UNLV Theses, Dissertations, Professional Papers, and Capstones

The concept of composite distributions was proposed in the early 2000s as a good parametric solution to model the data with heavy tails. Since the concept was proposed, it has been widely used in different areas, such as modeling insurance claim size data, predicting the risk measures in insurance data analysis, fitting survival time data, and modeling precipitation data. While a lot of the composite distributions demonstrated great performances in real applications, many commonly used composite distributions such as the inverse gamma-Pareto (IGP) or exponential-Pareto (EP), did not demonstrate great performances when fitting to several particular data sets. In order …