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Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan 2022 Southern Methodist University & Tandem Diabetes Care, Inc

Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan

SMU Data Science Review

Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs ...


Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler 2022 Southern Methodist University

Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler

SMU Data Science Review

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements ...


Improving Data-Driven Infrastructure Degradation Forecast Skill With Stepwise Asset Condition Prediction Models, Kurt R. Lamm, Justin D. Delorit, Michael N. Grussing, Steven J. Schuldt 2022 Air Force Institute of Technology

Improving Data-Driven Infrastructure Degradation Forecast Skill With Stepwise Asset Condition Prediction Models, Kurt R. Lamm, Justin D. Delorit, Michael N. Grussing, Steven J. Schuldt

Faculty Publications

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4 ...


Bias-Corrected Bagging In Active Learning With An Actuarial Application, Yangxuan Xu 2022 Western University

Bias-Corrected Bagging In Active Learning With An Actuarial Application, Yangxuan Xu

Undergraduate Student Research Internships Conference

The variable annuity (VA) is a modern insurance product that offers certain guaranteed protection and tax-deferred treatment. Because of the inherent complexity of guarantees’ payoff, the closed-form solution of fair market values (FMVs) is often not available. Most insurance companies depend on Monte Carlo (MC) simulation to price the FMVs of these products, which is an extremely computational intensive and time-consuming approach. The metamodeling approach can be used to circumvent the heavy computation.

In the modeling stage, the bagged tree method has proved to outperform other parametric approaches. Also, a bias-corrected (BC) bagging model was tried and showed significant improvement ...


The Q-Analogue Of The Extended Generalized Gamma Distribution, Wenhao Chen 2022 Western University

The Q-Analogue Of The Extended Generalized Gamma Distribution, Wenhao Chen

Undergraduate Student Research Internships Conference

This project introduces a flexible univariate probability model referred to as the q-analogue of the Extended Generalized Gamma (or q-EGG) distribution, which encompasses the majority of the most frequently used continuous distributions, including the gamma, Weibull, logistic, type-1 and type-2 beta, Gaussian, Cauchy, Student-t and F. Closed form representations of its moments and cumulative distribution function are provided. Additionally, computational techniques are proposed for determining estimates of its parameters. Both the method of moments and the maximum likelihood approach are utilized. The effect of each parameter is also graphically illustrated. Certain data sets are modeled with q-EGG distributions; goodness of ...


Investigation Of Key Factors To Earthquake Insurance Take-Up Rates In Quebec And British Columbia Households And Prediction Model Building, Yongcheng Jiang 2022 Western University

Investigation Of Key Factors To Earthquake Insurance Take-Up Rates In Quebec And British Columbia Households And Prediction Model Building, Yongcheng Jiang

Undergraduate Student Research Internships Conference

Maintaining an adequate level of earthquake take-up rate could protect the insurance industry from systemic failure. Past research has shown that British Columbia and Quebec have significant differences in earthquake insurance take-up rate. This report investigates key factors from the structure (default options and various types) of the insurance plan and personal characteristics along with socioeconomic/demographic profiles that affect the demand for earthquake protection in the form of insurance. The report also provides a prediction model for earthquake insurance take-up rate. The results show an importance ranking of key factors of earthquake insurance take up, the most important three ...


Financial Literacy: Self-Evaluation And Reality, Yangsijia Wang 2022 Western University

Financial Literacy: Self-Evaluation And Reality, Yangsijia Wang

Undergraduate Student Research Internships Conference

This study is on the topic of financial literacy, with the data source containing information on clients' demographic information and self-evaluation, change in account value, and trade record, three major problems were investigated: first, whether a client's demographic traits are related to his/her self-evaluation of financial knowledge level; second, does the trading behaviour differ for clients who self-identified as in different financial knowledge groups; and third, do people who self-identified as financially knowledgeable have better investment result. Data manipulation was done using SQL and R. Exploratory analysis including multiple types of plots and proportion tables was used to ...


Building Intrapersonal Competencies In The First-Year Experience: Utilizing Random Forest, Cluster Analysis, And Linear Regression To Identify Students’ Strengths And Opportunities For Institutional Improvement, Marilee Bresciani Ludvik, Shiming Zhang, Sandra Kahn, Nina Potter, Lisa Richardson-Gates, Stephen Schellenberg, Robyn Saiki, Nasima Subedi, Rebecca Harmata, Rey Monzon, Randy Timm, Jeanne Stronach, Anna Jost 2022 San Diego State University

Building Intrapersonal Competencies In The First-Year Experience: Utilizing Random Forest, Cluster Analysis, And Linear Regression To Identify Students’ Strengths And Opportunities For Institutional Improvement, Marilee Bresciani Ludvik, Shiming Zhang, Sandra Kahn, Nina Potter, Lisa Richardson-Gates, Stephen Schellenberg, Robyn Saiki, Nasima Subedi, Rebecca Harmata, Rey Monzon, Randy Timm, Jeanne Stronach, Anna Jost

Practical Assessment, Research, and Evaluation

Leveraging research that illustrates the importance of intrapersonal competency cultivation and its correlation with institutional performance indicators of student success such as end-of-term cumulative GPA, persistence, and academic probation, our team set out to conduct an analysis on the effectiveness of a 1-unit credit/no-credit first-semester, first-year student seminar course. The course was designed to cultivate specific intrapersonal competency gains using a pre- and post-assessment design. Using a supervised Random Forest method and cluster analysis, the team expected to find unique differences in intrapersonal competency pre-, matched pre- and post-, and post-assessment inventory scores in a way where course design ...


Practical T-Test Power Analysis With R, Teck Kiang Tan 2022 National University of Singapore

Practical T-Test Power Analysis With R, Teck Kiang Tan

Practical Assessment, Research, and Evaluation

Power analysis based on the analytical t-test is an important aspect of a research study to determine the sample size required to detect the effect for the comparison of two means. The current paper presents a reader-friendly procedure for carrying out the t-test power analysis using the various R add-on packages. While there is a growing of R users in the academic that uses R as the base for carrying out research, there is a lack of reference that discusses both frequentist and Bayesian approaches and point out their distinct features for t-test power analysis. The practical aspects of the ...


Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model, Jaehyeon Yun 2022 Southern Methodist University

Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model, Jaehyeon Yun

Statistical Science Theses and Dissertations

Alternating recurrent events data arise commonly in health research; examples include hospital admissions and discharges of diabetes patients; exacerbations and remissions of chronic bronchitis; and quitting and restarting smoking. Recent work has involved formulating and estimating joint models for the recurrent event times considering non-negligible event durations. However, prediction models for transition between recurrent events are lacking. We consider the development and evaluation of methods for predicting future events within these models. Specifically, we propose a tool for dynamically predicting transition between alternating recurrent events in real time. Under a flexible joint frailty model, we derive the predictive probability of ...


To Logit Or Not To Logit Data In The Unit Interval: A Simulation Study, Kayode Idris Hamzat 2022 University of Windsor

To Logit Or Not To Logit Data In The Unit Interval: A Simulation Study, Kayode Idris Hamzat

Major Papers

In this paper, we recommend a mechanism for determining whether to logit or not to logit data in the unit interval which is based on quantile estimation of data between 0 and 1. By using a simulated dataset generated from a Beta regression model, the estimated quantile for this model perform better than those based on the linear quantile regression with logit transformation.

Further, we investigate the performance of the quantile regression estimators based on the LQR and we conclude that it is better than those based on the Beta regression when the distribution is contaminated with 10% uniform numbers ...


Quantum Computing Simulation Of The Hydrogen Molecule System With Rigorous Quantum Circuit Derivations, Yili Zhang 2022 Utah State University

Quantum Computing Simulation Of The Hydrogen Molecule System With Rigorous Quantum Circuit Derivations, Yili Zhang

All Graduate Plan B and other Reports

Quantum computing has been an emerging technology in the past few decades. It utilizes the power of programmable quantum devices to perform computation, which can solve complex problems in a feasible time that is impossible with classical computers. Simulating quantum chemical systems using quantum computers is one of the most active research fields in quantum computing. However, due to the novelty of the technology and concept, most materials in the literature are not accessible for newbies in the field and sometimes can cause ambiguity for practitioners due to missing details.

This report provides a rigorous derivation of simulating quantum chemistry ...


Advanced High Dimensional Regression Techniques, Yuan Yang 2022 Clemson University

Advanced High Dimensional Regression Techniques, Yuan Yang

All Dissertations

This dissertation focuses on developing high dimensional regression techniques to analyze large scale data using both Bayesian and frequentist approaches, motivated by data sets from various disciplines, such as public health and genetics. More specifically, Chapters 2 and Chapter 4 take a Bayesian approach to achieve modeling and parameter estimation simultaneously while Chapter 3 takes a frequentist approach. The main aspects of these techniques are that they perform variable selection and parameter estimation simultaneously, while also being easily adaptable to large-scale data. In particular, by embedding a logistic model into traditional spike and slab framework and selecting of proper prior ...


Concerns With Taking The Covid-19 Vaccine, Kaela Bellamy, Robert S. Keyser 2022 Kennesaw State University

Concerns With Taking The Covid-19 Vaccine, Kaela Bellamy, Robert S. Keyser

The Kennesaw Journal of Undergraduate Research

This IRB-approved descriptive study provides an overview of the concerns associated with receiving a COVID-19 vaccination within the Kennesaw State University community, an R2 university with over 41,000 students, and uses a survey to provide insight into how students, faculty, staff, and administrators are responding to the vaccinations for COVID-19, both available and unavailable, and their preferences. Our research findings indicate that: 1) Most of the population at Kennesaw State University intends to receive the vaccine, regardless of their concerns; 2) The majority of the participants who are either employed or provided an education by Kennesaw State University plan ...


Adjusting Community Survey Data Benchmarks For External Factors, Allen Miller, Nicole M. Norelli, Robert Slater, Mingyang N. Yu 2022 Southern Methodist University

Adjusting Community Survey Data Benchmarks For External Factors, Allen Miller, Nicole M. Norelli, Robert Slater, Mingyang N. Yu

SMU Data Science Review

Abstract. Using U.S. resident survey data from the National Community Survey in combination with public data from the U.S. Census and additional sources, a Voting Regressor Model was developed to establish fair benchmark values for city performance. These benchmarks were adjusted for characteristics the city cannot easily influence that contribute to confidence in local government, such as population size, demographics, and income. This adjustment allows for a more meaningful comparison and interpretation of survey results among individual cities. Methods explored for the benchmark adjustment included cluster analysis, anomaly detection, and a variety of regression techniques, including random forest ...


(R1510) A Special Case Of Rodriguez-Lallena And Ubeda-Flores Copula Based On Ruschendorf Method, Marvin G. Pizon, Rolando N. Paluga 2022 Agusan del Sur State College of Agriculture and Technology

(R1510) A Special Case Of Rodriguez-Lallena And Ubeda-Flores Copula Based On Ruschendorf Method, Marvin G. Pizon, Rolando N. Paluga

Applications and Applied Mathematics: An International Journal (AAM)

Measure of dependence is a particular way of looking at the association between random variables, and one way to capture stochastic dependence is through the use of copula. In this study, a Rushendorf Method was applied to a bivariate function to obtain a copula through the use of a special case of Rodriguez-Lallena and Ubeda-Flores (RLUF) copula. Properties of the RLUF copula such as the density, measures of dependence, and lower and upper tail dependence were studied. In particular, measures of dependence such as Spearman’s rho, Kendall’s tau and Blomqvist’s beta of RLUF copula are given. Moreover ...


(R1503) Numerical Ultimate Survival Probabilities In An Insurance Portfolio Compounded By Risky Investments, Juma Kasozi 2022 Makerere University

(R1503) Numerical Ultimate Survival Probabilities In An Insurance Portfolio Compounded By Risky Investments, Juma Kasozi

Applications and Applied Mathematics: An International Journal (AAM)

Probability of ultimate survival is one of the central problems in insurance because it is a management tool that may be used to check on the solvency levels of the insurer. In this article, we numerically compute this probability for an insurer whose portfolio is compounded by investments arising from a risky asset. The uncertainty in the celebrated Cramér-Lundberg model is provided by a standard Brownian motion that is independent of the standard Brownian motion in the model for the risky asset. We apply an order four Block-by-block method in conjunction with the Simpson rule to solve the resulting Volterra ...


Evaluating A Statistical-Based Assessment Tool For Stratifying Risk Among U.S. Air Force Organizations, Tiffany A. Low 2022 Air Force Institute of Technology

Evaluating A Statistical-Based Assessment Tool For Stratifying Risk Among U.S. Air Force Organizations, Tiffany A. Low

Theses and Dissertations

The Air Force Inspection System is a proponent of utilizing a risk-based sampling strategy (RBSS) for conducting inspections from major command levels down to the unit level. The strategy identifies areas deemed most important or risky by commanders and prioritizes them accordingly for an independent assessment by the Inspector General. While Air Force regulation specifies the need to use a RBSS for inspection, the implementation process is delegated to individual commands and, subsequently, wings. The 23rd Wing, the sponsor for this research, directed us to analyze a RBSS tool highlighted as an example from which to adopt for those units ...


A Course In Data Science: R And Prediction Modeling, Adam Kapelner 2022 CUNY Queens College

A Course In Data Science: R And Prediction Modeling, Adam Kapelner

Open Educational Resources

This is a self-contained course in data science and machine learning using R. It covers philosophy of modeling with data, prediction via linear models, machine learning including support vector machines and random forests, probability estimation and asymmetric costs using logistic regression and probit regression, underfitting vs. overfitting, model validation, handling missingness and much more. There is formal instruction of data manipulation using dplyr and data.table, visualization using ggplot2 and statistical computing.


Model Averaging In Agriculture And Natural Resources: What Is It? When Is It Useful? When Is It A Distraction?, Philip M. Dixon 2022 Iowa State University

Model Averaging In Agriculture And Natural Resources: What Is It? When Is It Useful? When Is It A Distraction?, Philip M. Dixon

Conference on Applied Statistics in Agriculture and Natural Resources

I use two examples to illustrate three methods for model averaging: using AIC weights, using BIC weights, and fully Bayesian analyses. The first example is a capture-recapture study that estimates the population size by averaging over 4 models for capture probabilities. The second is an analysis of a study of logging impacts on Curculionid weevils using a before-after-control-impact (BACI) study design. The estimated impact is averaged over 4 ecologically relevant models.

Both examples demonstrate the sensitivity of model weights, or posterior model probabilities, to the choice of prior model probabilities and prior distributions for parameters. The model averaged estimates and ...


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