Statistical Models Commons^™

Functional Structure Of Excess Return And Volatility, Chenxi Zhao

Undergraduate Student Research Internships Conference

Capturing the relation between excess returns and volatility can help making better decisions in the stock market in terms of portfolio allocation and assets risk management. This paper takes the data of a minute-by-minute series of S&P500 from January 2009 to January 2021 as the research object and explores the best structural representation for the excess return as a function of the volatility, for a well-known index. This is implemented via regression models for volatility and excess returns. The results reveal that there’s a structural break in the relationship between the excess return and volatility based on the sign of …

Exploration In Mental Performance For Division 1 Sec College Football Student Athletes, Alex Burgdorf

Department of Occupational Therapy Entry-Level Capstone Projects

The stigma surrounding mental health in sports has made intervention difficult. “There is a need for various actors to provide more effective strategies to overcome the stigma that surrounds mental illness, increase mental health literacy in the athlete/coach community, and address athlete-specific barriers to seeking treatment for mental illness” (Castadelli-Maia et.al 2019). The athletes in the football program at the University of Tennessee face more pressure today than ever in history. They have their class schedule, practice and training every day, and meetings with their position coaches. Now, with the introduction of name, image, and likeness (NIL) allowing players to …

Abm Simulation Model Of A Pandemic For Optimizing Vaccination Strategy, Gibeom Park

Theses and Dissertations

This study presents a process-oriented hybrid model for individuals' immune responses and interactions involving vaccination to describe the trend of contagious disease and estimate the future societal cost. The model considers "recovery" as a non-absorbing state and incorporates various infection stage states including two symptomatic states. To model contagiousness to be consistent with the current pandemic and include that the spread of a disease depends on the mobility of people, we developed an Agent-Based Simulator that fitted to the particular model used in this study and can test various what-if scenarios. We improved the simulator considerably by appying data structures …

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 …

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 …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Statistical Methods For Personalized Treatment Selection And Survival Data Analysis Based On Observational Data With High-Dimensional Covariates., Don Ramesh Dinendra Sudaraka Tholkage

Electronic Theses and Dissertations

Due to the wide availability of functional data from multiple disciplines, the studies of functional data analysis have become popular in the recent literature. However, the related development in censored survival data has been relatively sparse. In Chapter 2, we consider the problem of analyzing time-to-event data in the presence of functional predictors. We develop a conditional generalized Kaplan Meier (KM) estimator that incorporates functional predictors using kernel weights and rigorously establishes its asymptotic properties. In addition, we propose to select the optimal bandwidth based on a time-dependent Brier score. We then carry out extensive numerical studies to examine the …

New Developments On The Estimability And The Estimation Of Phase-Type Actuarial Models, Cong Nie

Electronic Thesis and Dissertation Repository

This thesis studies the estimability and the estimation methods for two models based on Markov processes: the phase-type aging model (PTAM), which models the human aging process, and the discrete multivariate phase-type model (DMPTM), which can be used to model multivariate insurance claim processes.

The principal contributions of this thesis can be categorized into two areas. First, an objective measure of estimability is proposed to quantify estimability in the context of statistical models. Existing methods for assessing estimability require the subjective specification of thresholds, which potentially limits their usefulness. Unlike these methods, the proposed measure of estimability is objective. In …

Statistical Extensions Of Multi-Task Learning With Semiparametric Methods And Task Diagnostics, Nikolay Miller

Mathematics & Statistics ETDs

In this dissertation, I propose new approaches to multi-task learning, inspired by statistical model diagnostics and semiparametric and additive modeling. The newly designed additive multi-task model framework allows for flexible estimation of multi-task parametric and nonparametric effects by using an extension of the backfitting algorithm. Further, I propose new methods for statistical task diagnostics, which allow for the identification and remedy of outlier tasks, based on task-specific performance metrics and their empirical distributions. I perform a deep examination of the well-established multi-task kernel method and achieve theoretical and experimental contributions. Lastly, I propose a two-step modeling approach to multi-task modeling, …

Applications Of Machine Learning Algorithms In Materials Science And Bioinformatics, Mohammed Quazi

Mathematics & Statistics ETDs

The piezoelectric response has been a measure of interest in density functional theory (DFT) for micro-electromechanical systems (MEMS) since the inception of MEMS technology. Piezoelectric-based MEMS devices find wide applications in automobiles, mobile phones, healthcare devices, and silicon chips for computers, to name a few. Piezoelectric properties of doped aluminum nitride (AlN) have been under investigation in materials science for piezoelectric thin films because of its wide range of device applicability. In this research using rigorous DFT calculations, high throughput ab-initio simulations for 23 AlN alloys are generated.

This research is the first to report strong enhancements of piezoelectric properties …

A Bayesian Programming Approach To Car-Following Model Calibration And Validation Using Limited Data, Franklin Abodo

FIU Electronic Theses and Dissertations

Traffic simulation software is used by transportation researchers and engineers to design and evaluate changes to roadway networks. Underlying these simulators are mathematical models of microscopic driver behavior from which macroscopic measures of flow and congestion can be recovered. Many models are intended to apply to only a subset of possible traffic scenarios and roadway configurations, while others do not have any explicit constraint on their applicability. Work zones on highways are one scenario for which no model invented to date has been shown to accurately reproduce realistic driving behavior. This makes it difficult to optimize for safety and other …

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

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, ridge, decision …

Bootstrapped Fractional Designs Applied To Models With Both Mixture And Process Variables, Laura Vinton

Honors Theses

Mixture variables are unique as the components must sum to 1, causing problems when there is interaction between mixture and process variables. The best model is the fully linearized model, but this can get large quickly. We began by comparing models on multiple data sets. These models include linear and nonlinear models. After seeing that nonlinear models appear to be the best alternatives, we used the systematically selected fractions of each data set in order to obtain an in and out of sample RMSE. This allows us to see if there is evidence of overfitting, how well the model predicts …

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.

Statistical Characteristics Of High-Frequency Gravity Waves Observed By An Airglow Imager At Andes Lidar Observatory, Alan Z. Liu, Bing Cao

Publications

The long-term statistical characteristics of high-frequency quasi-monochromatic gravity waves are presented using multi-year airglow images observed at Andes Lidar Observatory (ALO, 30.3° S, 70.7° W) in northern Chile. The distribution of primary gravity wave parameters including horizontal wavelength, vertical wavelength, intrinsic wave speed, and intrinsic wave period are obtained and are in the ranges of 20–30 km, 15–25 km, 50–100 m s−1, and 5–10 min, respectively. The duration of persistent gravity wave events captured by the imager approximately follows an exponential distribution with an average duration of 7–9 min. The waves tend to propagate against the local background winds and …

Data Ethics: An Investigation Of Data, Algorithms, And Practice, Gabrialla S. Cockerell

Honors Projects

This paper encompasses an examination of defective data collection, algorithms, and practices that continue to be cycled through society under the illusion that all information is processed uniformly, and technological innovation consistently parallels societal betterment. However, vulnerable communities, typically the impoverished and racially discriminated, get ensnared in these harmful cycles due to their disadvantages. Their hindrances are reflected in their information due to the interconnectedness of data, such as race being highly correlated to wealth, education, and location. However, their information continues to be analyzed with the same measures as populations who are not significantly affected by racial bias. Not …

How Environmental Change Will Impact Mosquito-Borne Diseases, Arsal Khan

Master's Projects and Capstones

Mosquitos, the most lethal species throughout human history, are the most prevalent source of vector-borne diseases and therefore a major global health burden. Mosquito-borne disease incidence is expected to shift with environmental change. These changes can be predicted using species distribution models. With the wide variety of methods used for models, consensus for improving accuracy and comparability is needed. A comparative analysis of three recent modeling approaches revealed that integrating modeling techniques compensates for trade-offs associated with a singular approach. An area that represents a critical gap in our ability to predict mosquito behavior in response to changing climate factors, …

An Econometric Analysis Of Collegiate Player Performance To Create A Model For Forecasting Contributions To Team Success, Evan Seely

Undergraduate Theses

At the conclusion of each basketball season, each conference selects 1^st, 2^nd, and sometimes 3^rd all-conference teams based on player performance for that season. Often, these all-conference teams reflect biases in the media rather than evaluations based on player performance alone. The baseball statistic Wins Above Replacement, WAR, is useful in quantifying the impact of each player through the number of wins contributed to his respective team by comparing each player to a designated replacement level player. This statistic can also be applied to basketball analysis to perform a similar function as in baseball, despite …

Impact Of Climate Oscillations/Indices On Hydrological Variables In The Mississippi River Valley Alluvial Aquifer., Meena Raju

Theses and Dissertations

The Mississippi River Valley Alluvial Aquifer (MRVAA) is one of the most productive agricultural regions in the United States. The main objectives of this research are to identify long term trends and change points in hydrological variables (streamflow and rainfall), to assess the relationship between hydrological variables, and to evaluate the influence of global climate indices on hydrological variables. Non-parametric tests, MMK and Pettitt’s tests were used to analyze trend and change points. PCC and Streamflow elasticity analysis were used to analyze the relationship between streamflow and rainfall and the sensitivity of streamflow to rainfall changes. PCC and MLR analysis …