Parameter Optimization For Excitable Cell Models,
2023
Montclair State University
Parameter Optimization For Excitable Cell Models, Amrit Parmar
Theses, Dissertations and Culminating Projects
The electrophysiology of nodose ganglia neurons is of great interest in the analysis of cell membrane currents and action potential behavior. This behavior was initially outlined in the Hodgkin-Huxley conductance model [1] using a system of nonlinear differential equations. Later, Schild et al. [2] developed an extension of the Hodgkin-Huxley model to provide a more exhaustive description of ion channels involved in nodose neuronal action potential activity. We consider a variety of methods to fit the parameters of both the Hodgkin-Huxley and Schild et al. models to an empirical stimulus response dataset. Our methods were validated using synthetic datasets, as …
Subgroup Identification Via Interaction Tree And Mixed Model For Repeated Measures With Application To Alzheimer’S Disease,
2023
Washington University in St. Louis
Subgroup Identification Via Interaction Tree And Mixed Model For Repeated Measures With Application To Alzheimer’S Disease, Zhichen Xu
Arts & Sciences Graduate Student Theses and Dissertations
No abstract provided.
Inference For Multiple Utility In Time-Dependent Choice Pairs Under Copula-Based Models,
2023
Old Dominion University
Inference For Multiple Utility In Time-Dependent Choice Pairs Under Copula-Based Models, Sasanka Adikari
Mathematics & Statistics Theses & Dissertations
Models for discrete choice experiments (DCE) are frequently used to analyze consumer choices about products and services. A family of DCE, best-worst scaling experiments, offers more in-depth insights into consumer preferences by eliciting a best and worst choice from a set of options, rather than just a single preference. Traditional approaches often assume that choices are mutually exclusive over time, which may not always be the case. This dissertation proposes a novel model for DCE that takes into account the changing nature of consumer choices over time and the priority constraint of transition probabilities. The model introduces a copula combination …
Comparison Of Different Robust Methods In Linear Regression And Applications In Cardiovascular Data,
2023
University of Texas at El Paso
Comparison Of Different Robust Methods In Linear Regression And Applications In Cardiovascular Data, Jagannath Das
Open Access Theses & Dissertations
Due to advanced technology and wide source of data collection, high-dimensional data is available in several fields, including healthcare, bioinformatics, medicine, epidemiology, economics, finance, sociology, and climatology. In those datasets, outliers are generally encountered due to technical errors, heterogeneous sources, or the effect of some confounding variables. As outliers are often difficult to detect in high-dimensional data, the standard approaches may fail to model such data and produce misleading information. In this thesis, we studied Huber and Tukey's M-estimators for linear regression that automatically down-weight outliers and provide a good fit. We also investigated two variable selection methods -- LASSO …
Outlier Detection In Multivariate And High-Dimensional Datasets,
2023
University of Texas at El Paso
Outlier Detection In Multivariate And High-Dimensional Datasets, Yuanhong Wu
Open Access Theses & Dissertations
Accurate detection of outliers is crucial in the field of statistical analysis. Using classical statisticalmodels without considering the presence of outliers in the data can lead to misleading outcomes. There exist a myriad of procedures to detect outliers in statistics. We concentrate on the statistical techniques that can robustly identify outliers in data sets. To this end, we pursue two aims. First, we give an extensive overview of robust statistical methods which are still popular in recent years for outlier detection. We provide the definitions, algorithms and also discuss some important properties of these methods. Second, two real examples are …
A Machine Learning Approach To Evaluate The Effect Of Sodium-Glucose Cotransporter-2 Inhibitors On Chronic Kidney Disease In Diabetes Patients,
2023
The University of Texas Rio Grande Valley
A Machine Learning Approach To Evaluate The Effect Of Sodium-Glucose Cotransporter-2 Inhibitors On Chronic Kidney Disease In Diabetes Patients, Solomon Eshun
Theses and Dissertations
Chronic kidney disease (CKD) is a significant complication that contributes to diabetes-related mortality in the United States, and there is growing evidence that sodium-glucose cotransporter 2 inhibitors (SGLT2i) can slow its progression. However, observational studies may suffer from confounding by indication, where patient characteristics and disease severity influence the decision to prescribe SGLT2i. This study utilized electronic health records of individuals with diabetes (from TriNetX) to investigate the effectiveness of SGLT2i on CKD progression. The database provided detailed information on patients’ CKD status, demographics, diagnosis, procedures, and medications, along with corresponding dates of diagnosis and prescription. The study comprised of …
Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System,
2023
Clemson University
Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin
All Dissertations
Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …
Non-Destructive Imaging Of Phytosulfokine Trafficking In Plants Using Fiber-Optic Fluorescence Microscopy,
2023
East Tennessee State University
Non-Destructive Imaging Of Phytosulfokine Trafficking In Plants Using Fiber-Optic Fluorescence Microscopy, Bernard Abakah
Electronic Theses and Dissertations
Plants secrete peptide ligands and use receptor signaling to respond to stress and control development. Understanding these phenomena is key to improving plant health and productivity for food, fiber, and energy applications. Phytosulfokine (PSK), a sulfated peptide hormone, regulates plant cell division, growth, and stress tolerance via specific phytosulfokine receptors (PSKRs). This study uses fiber-optic fluorescence microscopy to elucidate trafficking of PSK in live plants. The microscope features two-color optics and an objective lens connected to a 1-m coherent imaging fiber mounted on either a conventional upright microscope body or 5-axis positioning system (X–Y–Z plus pitch and yaw). PSK and …
Jackknife Empirical Likelihood Tests For Equality Of Generalized Lorenz Curves,
2023
California State University, San Bernardino
Jackknife Empirical Likelihood Tests For Equality Of Generalized Lorenz Curves, Anton Butenko
Electronic Theses, Projects, and Dissertations
A Lorenz curve is a graphical representation of the distribution of income or wealth within a population. The generalized Lorenz curve can be created by scaling the values on the vertical axis of a Lorenz curve by the average output of the distribution. In this thesis, we propose two nonparametric methods for testing the equality of two generalized Lorenz curves. Both methods are based on empirical likelihood and utilize a U -statistic. We derive the limiting distribution of the likelihood ratio, which is shown to follow a chi-squared distribution with one degree of freedom. We conduct simulations to compare the …
Distance Correlation Based Feature Selection In Random Forest,
2023
California State University - San Bernardino
Distance Correlation Based Feature Selection In Random Forest, Jose Munoz-Lopez
Electronic Theses, Projects, and Dissertations
The Pearson correlation coefficient is a commonly used measure of correlation, but it has limitations as it only measures the linear relationship between two numerical variables. In 2007, Szekely et al. introduced the distance correlation, which measures all types of dependencies between random vectors X and Y in arbitrary dimensions, not just the linear ones. In this thesis, we propose a filter method that utilizes distance correlation as a criterion for feature selection in Random Forest regression. We conduct extensive simulation studies to evaluate its performance compared to existing methods under various data settings, in terms of the prediction mean …
Quantification Of Various Types Of Biases In Large Language Models,
2023
Kennesaw State University
Quantification Of Various Types Of Biases In Large Language Models, Sudhashree Sayenju
Doctor of Data Science and Analytics Dissertations
Natural Language Processing (NLP) systems are included everywhere on the internet from search engines, language translations to more advanced systems like voice assistant and customer service. Since humans are always on the receiving end of NLP technologies, it is very important to analyze whether or not the Large Language Models (LLMs) in use have bias and are therefore unfair. The majority of the research in NLP bias has focused on societal stereotype biases embedded in LLMs. However, our research focuses on all types of biases, namely model class level bias, stereotype bias and domain bias present in LLMs. Model class …
The 2015 Ncaa Cost-Of-Attendance Stipend And Its Effects On Institutional Financial Aid Packages,
2023
University of Richmond
The 2015 Ncaa Cost-Of-Attendance Stipend And Its Effects On Institutional Financial Aid Packages, Sara Greene
Honors Theses
In 2015, the National Collegiate Athletic Association (NCAA) allowed “Cost of Attendance” (COA) stipends to be offered to athletic recruits for Division I schools. These stipends are intended to allow schools to grant aid to student-athletes beyond a full-ride scholarship to cover additional costs imposed on student-athletes. These stipends created an opportunity for the “Autonomy” Power 5 programs to utilize a competitive tactic to try to win over the top recruits. There is evidence that these COA stipends have caused an increase in the estimated cost of attendance reported by the university. This paper examines if the COA stipends have …
Time Series Analysis Of Longitudinally Collected Standard Autoperimetry Data In Glaucoma Patients,
2023
Murray State University
Time Series Analysis Of Longitudinally Collected Standard Autoperimetry Data In Glaucoma Patients, Carlyn Childress
Honors College Theses
Glaucoma is a group of eye diseases in which damage gradually occurs to the optic nerve, which often leads to partial or complete loss of vision. As the second leading cause of blindness, there is no cure for glaucoma. Early detection and the tracking of its progression is key to managing the effects of glaucoma. Ordinary Least Squares Regression (OLSR), the most commonly used methodology for tracking glaucoma progression, is inappropriate as the longitudinally collected perimetry data from the glaucoma patients appears to be temporally correlated. Time series models, that account for temporal correlation, are better methods to analyze Mean …
On Cox Proportional Hazards Model Performance Under Different Sampling Schemes,
2023
Georgia Southern University, Jiann-Ping Hsu College of Public Health
On Cox Proportional Hazards Model Performance Under Different Sampling Schemes, Hani Samawi, Lili Yu, Jingjing Yin
Biostatistics, Epidemiology & Environmental Health Sciences: Faculty Publications
Cox’s proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models’ performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified Extreme, and Double Extreme Ranked Set Sampling (ERSS, and DERSS) schemes with a simple random sampling scheme. Observations are assumed to be selected based on an easy-to-evaluate baseline available variable associated with the survival time. Through intensive simulations, we show that these modified approaches (ERSS and DERSS) provide more powerful testing procedures and more efficient estimates of hazard ratio than those based on …
Employee Attrition: Analyzing Factors Influencing Job Satisfaction Of Ibm Data Scientists,
2023
Kennesaw State University
Employee Attrition: Analyzing Factors Influencing Job Satisfaction Of Ibm Data Scientists, Graham Nash
Symposium of Student Scholars
Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a …
Crime In Los Angeles,
2023
Kennesaw State University
Crime In Los Angeles, Cierra Hughley
Symposium of Student Scholars
This study will examine crimes committed in the city of Los Angeles dating back to the year of 2020. The reported data was pulled from the open data of Los Angeles Police Department. The purpose of this study is to show if gender is related to the three primary crimes: property crimes, violent crimes, or other crimes. Doing so will show which crimes were committed by each gender. Even though this study is on gender and crimes committed; it was a hard decision because there were many variables to choose from. However, exploring the relationship between crime and gender was …
Statistical Analysis Of The Relationship Between Protected Bird Species And National Parks,
2023
Kennesaw State University
Statistical Analysis Of The Relationship Between Protected Bird Species And National Parks, Katherine Harmon
Symposium of Student Scholars
The ecological diversity of Earth is majorly threatened by habitat loss due to the destruction by human intervention. The conservation status of all identified species are classified into nine categories of varying vulnerability as described by the International Union for Conservation of Nature’s Red List. By understanding the vulnerability of specific species, scientists can work to maintain a viable and healthy ecosystem globally by instilling rules and regulations of observed habitats for threatened species. These habitats are identified by surveying potential locations for threatened species and determining the population size at each site. An example of one of these surveys …
Defining Characteristics That Lead To Cost-Efficient Veteran Nba Free Agent Signings,
2023
Bryant University
Defining Characteristics That Lead To Cost-Efficient Veteran Nba Free Agent Signings, David Mccain
Honors Projects in Mathematics
Throughout the history of the NBA, decisions regarding the signing of free agents have been riddled with complexity. Franchises are tasked with finding out what players will serve as optimal free agent signings prior to seeing them perform within the framework of their team. This study hypothesizes that the adequacy of an NBA free agent signing can be modeled and predicted through the implementation of a machine learning model. The model will learn the necessary information using training and testing data sets that include various player biometrics, game statistics, and financial information. The application of this machine learning model will …
Reducing Restaurant Inventory Costs Through Sales Forecasting,
2023
Kennesaw State University
Reducing Restaurant Inventory Costs Through Sales Forecasting, Tyler Mason, Chris Schoen, Trevor Gilbert, Jonathan Enriquez
Senior Design Project For Engineers
Family Restaurant is a local restaurant in the greater Atlanta area that serves a variety of dishes that include an assortment of 19 different proteins. Currently, Family Restaurant places protein orders based on business intuition, and tends to over-stock and sometimes under-stock. To minimize inventory costs by reducing over-stocking and preventing under-stocking of proteins, we applied Facebook Prophet (FB Prophet), ARIMA, and XG Boost machine learning models to predict protein demand and then fed these results into a Fixed Time Period inventory model to make an overall order suggestion based on the specified time period. We trained our models on …
Two Sample Statistical Test For Location Parameters,
2023
Panjab University, Chandigarh
Two Sample Statistical Test For Location Parameters, Narinder Kumar, Arun Kumar
Journal of Modern Applied Statistical Methods
A class of distribution-free tests for the homogeneity of location parameters is proposed and compared with different competitors in terms of Pitman asymptotic relative efficiency. A numerical example is provided and a simulation study is made to check the performance of the tests.
