Physical Sciences and Mathematics Commons^™

Dynamic Overnight Effect On Next Day Stock Market Forecasting, Thomas J. Lee

Graduate Research Theses & Dissertations

Using a cross section of stocks that have high frequency trading data from 2007 to 2018, we document whether various intraday momentum patterns found in the financial literature over the years continue to hold over time. The first half hour return on the market is often seen as having predictive power over the last half hour of trading, or overnight returns are thought to reverse in the next day's first half hour of trading. We find that while there is some evidence for these patterns, especially in the earlier years, these patterns tend to weaken over time as investors take …

Applications For Functional Data Analysis, Kacy D. Kane

Graduate Research Theses & Dissertations

Functional Data Analysis is often used in the study of data that exists over a continuum, such as time. There are two datasets that will be considered here. For the first study we have a dataset on the efficacy of a lobectomy in reduction or elimination of epileptic seizures in patients. After an initial analysis of the dataset from a multinomial model perspective, we found that there were outliers in our dataset. From there, we considered a Multinomial Mixture Model to aid in the detection of outliers. In our second dataset we are considering a social robotics dataset where the …

Macroeconomic Factors Influencing Foreign Direct Investment In Some Selected Countries In Africa, Richard Essel Mensah

Graduate Research Theses & Dissertations

This paper investigates the possible factors that influence foreign direct investment inflow rate to Africa after controlling for other macroeconomic factors. Using the heterogenous Toeplitz mixed method on a sample of 23 countries from 1998 – 2020, we find evidence of the statistical significance of a relationship between the amount of trade done in Africa and the FDI inflow rate in Africa. We also find a statistical relationship between the labor force participation rate and the FDI inflow rate to Africa. Although the Fixed effect and GLM method did not find the relationship between LFP rate and FDI inflow to …

The Impact Of Faculty Composition On Cost Per Student: A Mixed Model Approach, Arun Sleeba

Graduate Research Theses & Dissertations

This thesis aims to explore whether research universities in the United States, specifically those classified as Carnegie I or II institutions, utilize part time contingent faculty(rPTF) as a cost saving strategy. Additionally, it sought to determine if there was a differential impact on total costs when comparing public and private universities. Employing a linear mixed effects model with random intercept and slopes, this study analyzed the relationship between rPTF (ratio of part-time to total faculty) and total cost. This study did not provide substantial evidence to support the notion despite observing a negative correlation between rPTF and total cost. Regarding …

A State Space Modeling Approach To Eeg Artifact Removal, Patrick B. Rafael

Graduate Research Theses & Dissertations

In this work, a state space modeling approach is applied to an Electroencephalography(EEG) recording for the purpose of artifact removal, and is compared against Independent Components Analysis (ICA), the current gold standard. Issues of model identifiability are touched on, and Hamiltonian Monte Carlo (HMC) is used to estimate a linear non-Gaussian state space model. Results show that estimating such a model is a nontrivial matter, and the full utility of the state space approach remains to be demonstrated.

Enhanced Maximum Likelihood Models For Underreported Variables: Extending To Multiple Claims Dimension, Shalaka Sudhanshu Sarpotdar

Graduate Research Theses & Dissertations

This thesis builds upon the foundations laid out in Xia et al. [2023], which explored the utilizationof Maximum Likelihood approach to model misrepresentation data in Generalized Linear Models (GLM) ratemaking models. We introduce the concept of “underreported variables”, a form of insurance misrepresentation where insured individuals provide inaccurate information about risk factors that influence insurance eligibility, premiums, and insured amounts. Unlike fraudulent misrepresentation, underreported variables arise from a lack of awareness regarding the insured’s mental and physical health conditions, rather than fraudulent intent. The study rigorously tests the proposed model using health insurance data and extends its applicability to other …

Comparative Transcriptomic Study Between Cyanobacteria That Contain Chlorophyll D And Those That Lack Chlorophyll D, Fernanda Montoya

Honors Capstones

All cyanobacteria, which perform oxygenic photosynthesis on Earth, contain the photosynthetic pigment chlorophyll a (Chl a) that absorbs light in the violet and red region of the visible spectrum. Cyanobacteria of the Acaryochloris species, however, contain the rare photosynthetic pigment chlorophyll d (Chl d) that absorbs light in the far-red region. Chl d’s ability to absorb light in this region allows it to avoid competing with other photosynthetic organisms for light. Creating a photosystem that uses Chl d in plants would be of great use for agricultural land optimization, but requires knowledge of the biosynthetic pathways of …

Factors Affecting Time To Recovery: A Covid-19 Survival Analysis, Fernanda Montoya

Honors Capstones

This project is focused on the recovery rates of patients diagnosed with COVID-19 after different clinical trial drug treatments. Data for the clinical trial studied was obtained from the National Institute of Allergy and Infectious Diseases for the primary purpose of a survival analysis on patient time to recovery under a placebo and therapeutic drug treatment. Specifically, patients in this clinical trial were randomly selected to receive remdesivir, an antiviral drug, in combination with a placebo or baricitinib, a janus kinase inhibitor drug. Cox PH models were used to identify how the different treatment drugs affect time to recovery and …

The Role Of Health Expenditure On Health Outcomes: Evidence In West Africa Countries., Festus Efosa Eriamiatoe

Graduate Research Theses & Dissertations

This study investigates the role of health expenditure on health outcomes in West Africa countries. Using the Grossman theoretical framework, other variables that affect health outcomes were also examined. Sixteen countries of West Africa with yearly data spanning between the period of 19 years (2000-2019) was used in this study. The health outcomes examined include life expectancy and neonatal and under-5 mortality rates. The regressors used in the study include domestic government health expenditure per capita, domestic private health expenditure per capita, external health expenditure per capita, carbon-dioxide emission metric ton per capita, human immunodeficiency virus (HIV), unemployment, GDP per …

Impact Of Public And Private Investments On Economic Growth Of Developing Countries, Faruque Ahamed

Graduate Research Theses & Dissertations

This paper aims to study the impact of public and private investments on the economic growth of developing countries. The study uses panel data from 39 developing countries covering the periods 1990-2019. The study is based on the neoclassical growth models or exogenous growth models in which land, labor, capital accumulation, etc., and technology proved substantial for economic growth. The paper uses the impact on overall GDP growth and GDP per capita growth. The study used a mixed-effect regression model and a Bayesian logistic regression model to derive the findings. For private investments, domestic credit has a positive association, but …

Forecasting Bitcoin, Ethereum And Litecoin Prices Using Machine Learning, Sai Prabhu Jaligama

Graduate Research Theses & Dissertations

This research aims to predict the cryptocurrencies Bitcoin, Litecoin and Ethereum using Time Series Modelling with daily data of closing price from 16th of October 2018 to 9th of September 2021for a total of 1073 days. Augmented Dickey Fuller test was first used to check stationarity of the time series, then two forecasting algorithms called ARIMA, and PROPHET were used to make predictions. The findings show similar results for both the models for each of Bitcoin, Ethereum and Litecoin. The results achieved show modelling cryptocurrencies which are volatile using a single variable produces satisfying results.

A Time Series Analysis Approach To Forecasting Covid-19 Cases And Deaths: An Analysis Of Covid-19 Data In Colombia, Andrea Jackson-Sagredo

Graduate Research Theses & Dissertations

The novel Coronavirus, known as COVID-19 is a highly contagious and transmissible infectious disease that has taken a toll throughout the entire world for over a year. The inner workings and long term effects of COVID-19 continue to be misunderstood. While COVID-19 has impacted all countries tremendously, Latin American countries and specifically Colombia have been impacted significantly by the virus. This thesis investigates the potential to forecast COVID-19 cases and deaths using Time Series Analysis methods and models for the South American country of Colombia. Time series analysis on Colombian COVID-19 data begins with data processing on a data set …

Optimization Of Dynamic Objective Functions Using Path Integrals, Paramahansa Pramanik

Graduate Research Theses & Dissertations

Path integrals are used to find an optimal strategy for a firm under a Walrasian system. We define dynamic optimal strategies and develop an integration method to capture all non-additive non-convex strategies. We also show that the method can solve the non-linear case, for example Merton-Garman-Hamiltonian system, which the traditional Pontryagin maximum principle cannot solve in closed form. Furthermore, we assume that the strategy space and time are inseparable with respect to a contract. Under this assumption we show that the strategy spacetime is a dynamic curved Liouville-like 2-brane quantum gravity surface under asymmetric information and that traditional Euclidean geometry …

Stochastic Infection In Network Models With Applications To Pollution Analysis, Alexander Thor Wold

Graduate Research Theses & Dissertations

The continued adoption and escalation of commercial surface extraction techniques threatens to contaminate adjacent river networks across the coal mining landscape. We look to simulate the movement of this pollution across connected graph structures using stochastic block model methodologies, fueled from work and theory derived from exponential random graph models. We begin our study by applying our virtual experiment to the motivating material, later offering an exhaustive walk through of the simulation itself. Afterwards, we present our findings and their implications to water pollution analysis, emphasizing a need for this research and expanding on some inferential statistics left for later …

A Glm Approach To Decomposing Wage Differential: Evidence From The Psid., Kassahun Mamo Geleta

Graduate Research Theses & Dissertations

The persistent gender wage differential, though declining through time, is the source of motivation to study the subject.A notable method to deal with the disparity is Oaxaca Blinder decomposition in combination with OLS estimation. This study follows a different approach that does not require the normality assumption and the log transformation of the wage variable. The study employs a generalized linear model (GLM) approach to estimate determinants of wage (measured in level) and combines the results with the Oaxaca Blinder decomposition method. The latter method quantifies the proportion of the wage gap which emanates from characteristics difference between men and …

Gene-Based Disease Classification Using Bayesian Self-Organizing Map Neural Networks, Guangting Zhou

Graduate Research Theses & Dissertations

Genes perform vital roles in living beings. By taking charges of protein synthesis, genes are able to take control of the expression of living traits. There are a lot of diseases associated closely to our genes. By analyzing genetic information, we are able to detect or classify gene based diseases. Among genetic disease information technologies, microarray can be one of the widely used ones. Usually, microarray data records thousands of gene expression features from a small number of samples including both normal and abnormal expressed tissues. It provides standardized comparison information between normal and diseased tissues, so as to provide …

Frequentist Methods In Handling Misrepresentation Risk, Rexford Mawunyegah Akakpo

Graduate Research Theses & Dissertations

A commonly encountered risk in insurance business is misrepresentation risk. Misrepresentation is a type of insurance fraud where a policyholder or a policy applicant falsifies his or her risk status in order to pay cheaper premiums for more expensive future risks. It is difficult and expensive for insurance companies to detect this kind of risk. With high cost of sophisticated underwriting, it becomes a norm for insurance companies to regularly rely on the policy applicant to self-report most of their risk statuses. We employ a frequentist approach by using expectation-maximization (EM) algorithm to carry out maximum likelihood estimation of the …

Robust Determinants Of Happiness: High-Dimensional Bayesian Treatment Of Model Uncertainty, Milivoje Davidovic

Graduate Research Theses & Dissertations

The thesis investigates the most relevant economic and institutional determinants of happiness in some 93 countries worldwide, covering the period 2006-2019. We employ the Bayesian Model Averaging (BMA) fixed effect model (country demeaned and time demeaned) using a working panel data set with 651 observations. Our initial goal is to address the problem of model uncertainty in panel data models of happiness, aiming at selecting a set variables that are likely to be included in as "true" model of happiness. In addition, we aim to investigate the causal relationship running from selected economic and institutional variable to index of happiness. …

Traffic Fatality Rate Prediction Based On Deep Neural Network And Bayesian Neural Network, Yiqun Hu

Graduate Research Theses & Dissertations

There have been numerous studies on traffic accidents and their fatality rate. For this challenging machine learning regression problem, Neural Networks (NNs) have produced state-of-the-art data. Despite their success, they are often used in a fre- quentist scheme, which means they cannot account for uncertainty in their forecasts. BNNs are comprised of a Probabilistic Model and a Neural Network. The aim of such a design is to bring together the benefits of Neural Networks and stochastic modeling. Neural networks have the ability to approximate continuous functions uni- versally. Statistical models allow for the direct definition of a model with known …

The Relevance Of Credit Risk In The Determination Of Commercial Banks’ Profitability: Evidence From Ghana, Godwin Kwabla Ekpe

Graduate Research Theses & Dissertations

Existing empirical literature on the relationship between credit risk and bank’s profitability is replete with mixed results. This research investigates the probable effect of credit risk on banks’ profitability by examining the nature of the relationship between two measures of credit risk (Loss provisioning rate and Actual provisioning charge rate) and two measures ofprofitability (Return on assets and Return on Equity). The investigation is conducted using data on the Ghanaian banking industry. Various modeling techniques are used to fit the data, including frequentist beta regression and Bayesian beta regression models. The results across all models suggest negative linear relationship between …

The Simulation Extrapolation Method With Differential Measurement Error, Dominic Partipilo

Graduate Research Theses & Dissertations

Most of statistical theory operates under the assumption that the true values of covariates have been measured correctly, but it is not always possible to obtain the true values of these covariates. A common issue, specifically in regression models, is that predictors are misclassified or measured with systematic measurement error. There have been many methods developed for handling measurement error, specifically in the case where measurement error is nondifferential, where the measurement error can be treated as independent from the covariates. The frequentist method known as simulation extrapolation (SIMEX) is one of these methods that specifically handles the case for …

Bayesian Tail Probability Estimation And Model Selection, Nan Shen

Graduate Research Theses & Dissertations

Bayesian statistics is a prevalent and important field in statistics that assigns Bayesian probabilities, which represent a state of knowledge, to unknown quantities. We study Bayesian statistics with its applications through two projects in this report.

In the first project, we investigate the reasons that the Bayesian estimator of the tail probability is always higher than the frequentist estimator. Sufficient conditions for this phenomenon are established by looking at Taylor series approximations about the tail and by using Jensen's Inequality, both of which point to the convexity of the distribution function.

The second project is about redefining the Bayesian information …

Bayesian Approach To Finding The Most Likely Circuit Structure, Shannon Harms

Graduate Research Theses & Dissertations

Systems, and their reliabilities, depend on the reliabilities of the components that theyare composed of, and in this paper we want to nd the system structure that is the most likely given observed data. Bayesian methods were utilized in order to discover the posterior means, or observed reliabilities, of both the components and the systems. Assuming the serial and parallel system structures have independent components, we calculated system reliabilities based on observed component reliabilities by using the multiplication and addi- tion probability rules. We are then able to expand upon the numerical comparison method through a maximum likelihood analysis that …

Construction Of Confidence Intervals For Parameter Estimates Of T-Distribution, Margaret Remus

Graduate Research Theses & Dissertations

Constructing confidence intervals is a standard statistical practice to estimate parameters of a distribution. Estimation of the parameters of a t-distribution can be computationally intensive. As a result, there are not clear defined formulas for the construction of confidence intervals for those estimates. Using the Normal confidence interval equation does not offer an accurate interval. Therefore, this paper will outline potential alternative methods for building confidence intervals to estimate the mean of a t-distribution. This paper will offer several methods and simulations to determine which method is best for constructing confidence intervals for varying sample sizes and degrees of freedom. …

Polarization In Social Networks Under A Proposed Model, Ashley L. Seyfried

Graduate Research Theses & Dissertations

The mathematical study of opinion dynamics started in 1956, with French. Since the explosion of social media, this study has increased in popularity. In this paper, we propose a new model to apply to a social network graph. With this model, the opinions of actors are in the range [0,1]. We use the Normal probability distribution to model these opinions in conjunction with a group of parameters. We allow for the uncertainty of members in the network. We then ran simulations to find the values of these parameters that do not lead to a network becoming polarized. Different states of …

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 …