Multivariate Analysis Commons^™

New Bayesian Methods For Longitudinal Data Analysis With Complex Dependence Structures., Anish Mukherjee

Electronic Theses and Dissertations

Longitudinal data in real-world settings are frequently found to be heterogeneous and exhibit intricate spatio-temporal dependence structures. Analyzing such complex data to obtain reliable estimation while quantifying uncertainty necessitates using sophisticated Bayesian methodology. In this work, we present novel Bayesian methods developed to address these challenges. We often observe heterogeneity in longitudinal data, where the mean and variance for certain profiles meaningfully differs from the rest. Some profiles may also exhibit outliers at a limited number of measurements. Using a standard mixed effects model, which assumes homogeneity, can lead to overestimating the residual variance and inefficient estimation. In this work, …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Mortgage Default Classification Modeling For Variable Analysis, Brendan R. Goggins

Honors College Theses

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables …

A Text Mining And Sentiment Analysis Of Valuable Cie Texts Using R, Eric Sugarman, Ethan Turber-Ortiz, Hannah Quinn

Mathematics, Computer Science & Statistics Presentations

The purpose of this project was to perform a sentiment analysis of three texts used in Ursinus College's Common Intellectual Experience (CIE) course: Between the World and Me by Ta-Nehisi Coates, The New Jim Crow by Michelle Alexander and Discourse on Method by Rene Descartes. Word count and word cloud analysis were also performed on the texts as well as term frequency and bigram analysis.

Impacts Of Extreme Weather Conditions On Coastal Fisheries Near Bayou Teche, Louisiana From 2019–2023, Georgia C. Davis

LSU Master's Theses

The region of Southcentral Louisiana, particularly around Bayou Teche, thrives on its commercial and recreational fisheries. These fisheries are occasionally subject to events like extreme weather that cause sudden and unexpected losses (NOAA Fisheries, 2024a). Between 2019 and 2023, a series of extreme weather events impacted southcentral Louisiana near Bayou Teche. This series of extreme events includes Hurricane Barry (2019), Hurricane Laura (2020), Hurricane Delta (2020), Hurricane Zeta (2020), Hurricane Ida (2021), and a United States Drought Monitor (USDM) D4 drought (2023). This study analyzes the impacts of the extreme weather series on coastal fish observed species richness (SR) in …

A Machine-Learning Tool-Supported Methodology For Nonprofit Donor Analysis, Corbin Weiss

Campus Research Month

We developed a machine-learning tool-supported methodology for modeling the nonprofit donor relationship. This approach was demonstrated in the case of a US-based nonprofit. Conclusions were drawn from this example and tool-support provided for use by other nonprofits.

Simulation Research On Financial Competitive Intelligence Early Warning Mechanism Of New Ventures Based On Big Data Thinking, Keping Wang, Jingyi Zhou, Yao Che, Zhen Qiao

Journal of Scientific Information Research

[Purpose/significance]Financial competitive intelligence is a core element in promoting the stable development of new ventures. Based on big data thinking, exploring the construction process and simulation effects of financial competitive intelligence warning mechanisms for new ventures is of great significance for improving their competitiveness and promoting healthy development. [Method/process]The article takes startups as the research object, constructs a financial competitive intelligence early warning mechanism for startups based on big data thinking, and uses system dynamics methods to construct causal relationship diagrams and system flow diagrams, and simulates them using Vensim PLE software. [Result/conclusion]The system dynamics model constructed in the article …

Urban Heat Dynamics In Pune: The Influence Of Land Cover And Local Climate, Arpit Tiwari, Preethi Nanjundan, Ravi Ranjan Kumar, Ananya Karmakar, Satyaban Bishoyi Ratna

Northeast Journal of Complex Systems (NEJCS)

Urban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (T_max; 0.13℃ to 1.63℃ …

Analysis Of Systematic Trade-Offs Between Military And Healthcare Expenditure Alongside Gdp Growth Of Select Asian And Western Exporting Economies In The 21st Century, Rahul Balamurugan, Carlos Gershenson, Preethi Nanjundan, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality …

Kroger Post-Pandemic Customer Segmentation, Mario Mata, Joey Truitt, Renn Spigelmyer, Dhanuja Kasturiratna, Lisa Holden, Nitish Baidya, Hanna Tafari

Posters-at-the-Capitol

The grocery retail industry landscape has changed greatly in the wake of the pandemic. Specifically, delivery and pickup services have become more popular and customer buying habits have evolved. At the same time, improvements in data collection and analysis have allowed grocery marketing strategies to become highly individualized.

We worked with 84.51, an analytics firm, to identify customer segments for the Kroger Company based on data from 2023. Using clustering techniques, we organized customers into groups, or segments, based on similar characteristics. We identified and profiled four distinct groups of customers. Three segments were characterized by high frequency and spending …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

An Insight Into Mediation Analysis, Nicholas Mccracken

Williams Honors College, Honors Research Projects

Mediation is an ideology often present in the social sciences. A mediator is meant to serve as the middle point between one party and another, taking the communications from one party and ensuring that the other party can comprehend that of the original party. Though this is very popular in social sciences, we can apply a statistical concept to it as well. We can explain the relationship between two parties and a mediator through a series of statistical equations, known as Baron and Kenny’s Equations. With these equations, we can determine how one variable is meant to impact another variable …

Hybrid Mixtures Of Factor Analyzers For High Dimensional Data, Kazeem Abiodun Kareem

Dissertations, Master's Theses and Master's Reports

Factor analysis is a powerful tool for modeling latent structures in high-dimensional data, traditional approaches assume a single global structure, limiting their ability to capture heterogeneity. The Mixture of Factor Analyzers (MFA) extends classical factor analysis by modeling data as a mixture of Gaussian-distributed local subspaces, effectively uncovering cluster-specific latent structures. However, MFA relies on Gaussian mixtures, making it sensitive to outliers and ill-suited for heavy-tailed data. The Mixture of $t$-Factor Analyzers (M$t$FA) addresses these limitations by incorporating multivariate $t$-distributions, improving robustness. Despite their advantages, both MFA and M$t$FA face significant computational challenges in high-dimensional settings, particularly due to costly …

Partisan Divides In Environmental Spending Attitudes: A Two-Level Hierarchical Analysis, 1973-2022, Jordan Lipner

Honors Undergraduate Theses

Public attitudes toward environmental spending have become increasingly divided along party lines, with sharp shifts over the past five decades. This thesis updates and expands on Johnson and Schwadel’s 2019 study by applying a two-level hierarchical linear model to General Social Survey data updated to include data from 2015-2022, capturing how political affiliation, education, race, and economic context interact with broader political and economic contexts to shape environmental attitudes over time.
The results show that political affiliation remains the strongest and most reactive predictor of environmental spending attitudes. Republican respondents are significantly more likely to oppose environmental spending, especially under …

Efficient Development Of Density-Insensitive Near-Infrared Methods For In-Line Drug Content Monitoring In Continuous Powder Streams, Natasha L. Velez-Silva

Electronic Theses and Dissertations

A well-defined action plan to respond effectively to sudden changes in product demand is critical for preventing drug shortages within the pharmaceutical industry. An effective way to increase the output of a continuous manufacturing (CM) process is through flow rate adjustments. However, robust analytical methods must be in place to ensure consistent analytical performance across varying flow rates. Existing approaches for mitigating the physical effects of flow rate on Near-Infrared (NIR) measurements are often burdensome. Thus, efficient robust modeling strategies that reduce the current calibration burden and ensure model insensitivity to the physical variations in CM systems are needed. In …

Calculation And Statistical Analysis Of Wins Above Replacement, Joshua Taylor

Departmental Honors & Graduate Capstone Projects

The Wins Above Replacement (WAR) statistic in Major League Baseball is a prominent metric used to estimate player value by quantifying all aspects of play in terms of wins added to a baseball team. We will use R to calculate WAR for all players from 1871 to 2012 and use data from those years to construct multivariate predictive models to attempt to estimate WAR for players from 2013 to 2024. We find strong correlations between predicted and actual WAR values for most models, with the exception of the polynomial predictive model for non-qualified pitchers.

If You Can’T Beat Them Join Them: Empirical Assessment Into How Integrating Conventional Taxis On The Uber App Impacts Conventional Taxi Ridership, Shahmeer Mohsin

CBER Conference

Since the emergence of ride-hailing platforms like Uber, conventional taxi ridership has taken a severe hit. Taxi-hailing apps like Curb and Arro have allowed conventional taxis to jump on the platform economy bandwagon and offer a similar service to ride-hailing platforms. Despite the emergence of these taxi-hailing apps, strong lock-in effects and high switching costs of popular ride-hailing platforms (Uber, Lyft, etc.) restrict the ridership volumes of conventional taxis. Recently, the ride-hailing platform, Uber has started to add conventional taxis on its app under increasing pressure from Cities and conventional taxi associations. Such integrations have the potential of increasing conventional …

Investigating Nekton Response To Changing Salinities In The Mississippi Sound: An Experimental And Statistical Approach, Adam Murray

Master's Theses

The Mississippi Sound provides nursery habitats for many coastal species and is recognized for its commercial fisheries. Previous freshening events linked to the Bonnet Carré Spillway, a Mississippi River flood diversion structure, proved catastrophic for oyster populations in the Mississippi Sound, but effects on mobile fish and shellfish species are not well-defined. The planned Mid-Breton Sediment Diversion (MBSD), an initiative to combat wetland loss, is also forecasted to lower salinities in the region, increasing the need to better understand responses of commercially and ecologically important species to freshening events. The objective of this study was to characterize effects of salinity …

Bayesian And Deep Generative Modeling In Immunology, Yuqiu Yang

Statistical Science Theses and Dissertations

Due to the accumulation of a large volume of data of different natures such as sequencing data, proteomics data, and clinical data, statistical methods and deep learning algorithms have become increasingly important in the field of immunology. By leveraging the diverse datasets as well as interdisciplinary knowledge from areas like biology and public health, these quantitative methods have revolutionized this field by providing powerful tools for data analysis, modeling, and prediction. This has led to a deeper understanding of the immune system, accelerated the development of novel therapies, and paved the way for personalized and precision medicine approaches in immunology. …

Gradient Wild Bootstrap For Instrumental Variable Quantile Regressions With Weak And Few Clusters, Wenjie Wang, Yichong Zhang

Research Collection School Of Economics

We study the gradient wild bootstrap-based inference for instrumental variable quantile regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed, and the number of observations for each cluster diverges to infinity. For the Wald inference, we show that our wild bootstrap Wald test, with or without studentization using the cluster-robust covariance estimator (CRVE), controls size asymptotically up to a small error as long as the parameter of endogenous variable is strongly identified in at least one of the clusters. We further show that the wild bootstrap Wald test …