Longitudinal Data Analysis and Time Series Commons^™

Evaluating Alpha Spending Functions Applied To Observational Time-To-Event Analysis, Moses Torgbenu

Electronic Theses and Dissertations

This thesis explores the theoretical foundation of the alpha spending approach and extends its application beyond the conventional setting of randomized controlled trials (RCTs) to observational studies with time to event analyses. In these less structured environments, key design parameters such as the total number of events are often unknown, posing challenges for the standard implementation of sequential analysis methods.

Through simulation studies, this research delivers several important contributions. First, it presents a modified approach that uses calendar time to define the timing of interim analyses while relying on event-based information to estimate the correlation among test statistics. This adjustment …

A Data-Driven Approach To Time Series Forecasting And Clustering Of U.S. Regional Drug Overdose Mortality, Koshali Hamy Muthunama Gonnage

Mathematics & Statistics ETDs

The increasing rate of drug overdose deaths in the United States poses a critical public health challenge, particularly due to the surge in synthetic opioids and other high-risk substances. This study presents a data-driven framework that integrates time series forecasting and clustering techniques. Monthly mortality data for five key drug types: cocaine, fentanyl, heroin, methamphetamine, and oxycodone were analyzed using four time series forecasting models: ARIMA, ETS, TBATS, and NNAR. These models were evaluated using standard accuracy metrics RMSE, MAPE, and MAE to assess predictive performance. Signal decomposition approach based on Singular Value Decomposition and subspace modeling was employed to …

The Nature Of Anthropogenically Driven River Drying: Spatiotemporal Causes And Consequences, Eliza Inez Gilbert

Biology ETDs

Streambed drying naturally occurs in over 60% of rivers and streams worldwide. Climate change and human regulation of surface and groundwater have increased drying in naturally intermittent systems and caused perennial systems to transition to intermittency, impacting water security, water quality, and biodiversity. To understand human-induced drying dynamics, we used 12 years of daily drying data along a 154-km regulated reach of the Rio Grande. We conceptualized river drying as a regime analogous to the natural flow regime paradigm and quantified drying magnitude, rate of change, and duration. Although linear models predicting drying magnitude and rate of change were uninterpretable, …

Conceptualizing The Explanatory Fully Longitudinal Mixed Methods Case Study Design: A Demonstration With An Arithmetic Education Trial With Kindergarten Children, Menglong Cong

Electronic Theses and Dissertations

In employing longitudinal mixed methods designs, researchers have commonly used the fully longitudinal mixed methods design. This has occurred mostly in the health sciences but less in education. The current investigation proposes a novel longitudinal mixed methods research design, explanatory fully longitudinal mixed methods case study design. It demonstrates its potential for addressing research inquiries in educational research using the arithmetic learning trajectories datasets. This study is presented in three components. The first is a quantitative phase, selecting an exploratory case from a previous arithmetic learning trajectories study (Clements et al., 2021) for qualitative analyses based on maximizing the …

Bandwagon Behavior In Major League Baseball, Daniel E. Erro

Master's Theses

This study investigates “bandwagon” behavior among Major League Baseball (MLB) fans by analyzing Google search interest data from 2004 to 2019. Drawing on publicly available information from Google Trends, the analysis explores how fluctuations in search activity align with team performance during both the regular season and postseason. Hierarchical linear models are used to estimate expected levels of fan interest based on team performance and market characteristics. Deviations from these expectations during the regular season are interpreted as evidence of bandwagon or anti-bandwagon behavior. A drop-off in interest following playoff elimination is also examined to capture shifts in fan attention …

Hybrid Graph-Recurrent Architecture For Citation Recommendation Via Future Embedding Forecasting, Mohammad Ausaf Ali Haqqani

Computer Science and Engineering Theses and Dissertations

The rapid expansion of scientific literature has intensified the challenge of identifying relevant citations, particularly for newly published or under-cited papers. Traditional citation recommendation systems typically model static relationships or respond to past citation activity, offering limited predictive power for emerging works. In response, this thesis presents a temporal modeling framework for citation recommendation that anticipates future scholarly relevance by forecasting the latent representations of academic papers.

Building on prior work that utilized Temporal Graph Networks (TGNs) to model dynamic citation flows, we propose Graph-Time, a hybrid architecture that integrates a Graph Transformer with a GRU-based time series predictor. The …

Using A Pharmacokinetic Model To Design And Evaluate An Early Ctdna Biomarker For Response To Targeted Therapy, Aaron Li

Spora: A Journal of Biomathematics

Early prediction of response to therapy or lack thereof can help physicians plan treatment more efficiently. Biomarkers based on circulating tumor DNA (ctDNA) are promising. However, biomarkers beyond direct comparison to baseline have not been thoroughly explored. We develop a model for ctDNA shedding under targeted therapy that incorporates pharmacokinetics. Using a simulated cohort of virtual patients with varied parameters, we define and analyze a biomarker based on ctDNA samples at baseline, 12 hours, and 24 hours after initiation of treatment. The biomarker identified patients who would achieve partial or complete response with high sensitivity and specificity and was able …

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, …

Enhancing Animal Shelter Operations With Time Series And Machine Learning, Sakava L. Kiv, Donald L. Anderson, Shivam Negi, Jacquelyn Cheun

SMU Data Science Review

Enhancing animal shelter operations through machine learning involves employing a variety of advanced techniques aimed at increasing efficiency, promoting animal welfare, and optimizing resource allocation. This paper explores predictive analytics for adoption rates using regression models to estimate the likelihood of adoption based on historical data, encompassing variables such as breed, health status, and previous adoption trends. Additionally, classification algorithms are utilized to categorize animals by adoption probability, facilitating better resources and marketing prioritization. Clustering algorithms are employed to group animals according to behavior patterns and/or physical health, enabling tailored medical care and enrichment activities that improve their mental and …

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.

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℃ …

Filters For Forecasting Crop Health: Analyzing And Projecting The Temporal Evolution Of Landsat Ndvi Data Using Dynamic Linear Models And The Kalman Filter, Kamal Albousafi, Hossein Moradi, Jung-Han Kimn

SDSU Data Science Symposium

Accurately forecasting food availability is a critical task. One approach involves utilizing remote sensing data, such as satellite images, to observe the health of crop fields using different Vegetation Indices (VI). The Normalized Difference Vegetation Index (NDVI) provides a sound metric to track the “greenness” of crops over time. In this research, we develop statistical models that capture the dynamics of NDVI time series data to make better predictions of its future values. The median NDVI of the pixels of a farm located in Edmunds County, South Dakota, is obtained using imagery from the Landsat 5 and Landsat 8 satellites, …

Beyond Homogeneity: Exploring Causal Heterogeneity In Psychopathology, Philip B. Vinh

Theses and Dissertations

Traditional models in psychiatric research often impose assumptions of causal homogeneity, treating population-level associations as reflective of uniform underlying mechanisms. This dissertation challenges that assumption by introducing statistical and machine learning frameworks designed to detect and model causal heterogeneity in the development of psychopathology. Central to this approach is the advancement of finite mixture structural equation modeling (FM-SEM) to identify latent subgroups characterized by distinct, and sometimes opposing, causal pathways.

The dissertation comprises three integrated empirical studies. The first introduces mixDoC, a finite mixture extension of the classical Direction of Causation (DoC) model applied to twin data, enabling the detection …

Crime Modeling Using An Integrated Cnn–Lstm Architecture With Embedded Self-Excitation, Pawandeep Kaur

Theses and Dissertations (Comprehensive)

It is often assumed that natural phenomena occur randomly over time. However, careful analysis reveals that these events typically form some series or sequences and exhibit distinctive temporal patterns. These patterns are not exclusive to nature. They also appear in human activities, often studied under the concept of bursty human dynamics. The statistical methods analyzing bursty human dynamics not only capture overall trends or seasonality but also explore how past events influence future ones. It makes the analysis more realistic and the results more closely aligned with reality. Bursty human dynamics can be studied at two levels: the individual level …

Time Series Modeling Of Akron Air Quality Index (Aqi) Data, Mason Yurich

Williams Honors College, Honors Research Projects

With the increase in population and industrialization around the world, climate has become a major concern for many researchers. One measure that has drawn much interest is air quality. There are available resources that track the Air Quality Index (AQI) in most large cities, but there is a general lack of information regarding Air Quality forecasts, even for one day in the future. This project aims to find a useful statistical model for representing and predicting the AQI measure in Akron, Ohio over time. By using historical air quality data from the United States Environmental Protection Agency and AQI.in, an …

A Time Series Analysis Of The Macroeconomic Indicators, Mia Houston

Honors Undergraduate Theses

Understanding inflation—particularly across regions and categories—is crucial for effective policymaking, strategic business decisions, and safeguarding vulnerable populations, as it highlights the diverse drivers and impacts of price changes within the economy. This has become increasingly crucial in recent years between the volatile inflation conditions introduced by the COVID-19 pandemic, energy price shocks, and renewed trade tensions and tariffs. This thesis analyzes 77 U.S. monthly inflation time series from 2003 to 2023 using two forecasting approaches: an elementwise Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a Factor-Augmented Vector Autoregressive (FAVAR) model. The data obtained from the Bureau of Labor Statistics …

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 …

Complex Missing Data Problems In Education Surveys, Thomas Wesley Robertson

Electronic Theses & Dissertations (2024 - present)

Missing data are a nearly universal problem in human subjects research, including in education. However, reporting and addressing missing data is an issue, despite guidelines from the APA style guide and the What Works Clearinghouse, as well as guidance from prominent statisticians on the best methods to use. Prior research conducted in 2004 and 2014 found that in the field of education, most studies do not report or address missing data. In addition, no study has looked specifically at how missing data are reported and addressed in complex surveys. The current study has two main objectives: first, to determine if …

Theoretical Foundations And Applied Performance Of Periodicity-Aware Imputation: Variable Bandpass Block Bootstrap Methods For Incomplete Time Series, Asmaa Ahmad

Electronic Theses & Dissertations (2024 - present)

Time series data are prevalent across a wide range of disciplines, including health surveillance, public policy, and environmental monitoring. In the presence of underlying cyclical patterns, the integrity of time series analysis depends critically on the ability to detect, model, and impute structured missing data without compromising the temporal structure. This dissertation introduces and validates a novel imputation framework that integrates the Variable Bandpass Periodic Block Bootstrap (VBPBB) into multiple imputation procedures, improving the accuracy, robustness, and interpretability of time series models under high rates of missingness and noise. The overarching goal of this dissertation was to develop and evaluate …

Mean From Median Estimation In Longitudinal Meta-Analysis Of Quality Of Life In Radiation Oncology Patients, Harlan R. Sayles

Theses & Dissertations

Meta-analysis of longitudinal data, where some of the studies report results with means and standard errors while others use medians and ranges, is a complex analytical problem with several challenges which must be overcome. Existing methods for estimating means from medians and either ranges, interquartile ranges, or both have not previously been evaluated in a longitudinal setting. In this work, a simulation study was used to estimate mean bias, between studies variance bias, and coverage of confidence intervals in a longitudinal setting. A second simulation study estimated the variance of meta-analysis results from a given set of studies that may …

Striking A Balance: Market Shock & Responses In Automotive Components Manufacturing, Emma Lane Mcgahey

All Theses

This thesis examines the effects of extreme market shocks on supply chain dynamics within the automotive industry. Through an analysis of demand data from an automotive manufacturer to its component suppliers (January 2018 to May 2024), the study investigates the relationship between market shocks and supply chain responses, providing insights into how auto components inventory management handles downstream responses to market shocks. With supporting public data—from FRED, BLS, and the U.S. Census Bureau resources—we explore two primary relationships: the impact of market shocks on the Average Standard Deviation of Demand (SDO) and the effect of demand variability on expedited pricing …

Policies And Price Controls On The Research And Development Of Orphan Drugs In The United States And The European Union, Bena Pearl Filipczak Smith

Master's Theses

There is substantive literature surrounding the impact of price controls on the research and development (R&D) of new pharmaceutical products. The European Union (EU) and United States (US) are often studied in contrast to examine the influence of price controls as the US has fewer pharmaceutical price controls than the EU.

We find moderate evidence that the US spent more on annual domestic pharmaceutical R&D than the EU between 2004 and 2021, on average, before and after adjusting for GDP growth per capita and year. We find strong evidence that the US increased annual domestic R&D spending at a faster …

Human Capital At Home: Evidence From A Randomized Evaluation In The Philippines, Noam Angrist, Sarah Kabay, Dean S. Karlan, Lincoln Lau, Kevin M. Wong

Education Division Scholarship

Children spend most of their time at home in their early years, yet efforts to promote human capital at home in many low- and middle-income settings remain limited. We conduct a randomized controlled trial to evaluate an intervention which encourages parents and caregivers to foster human capital accumulation among their children between ages 3 and 5, with a focus on math and phonics skills. Children gain 0.52 and 0.51 standard deviations relative to the control group on math and phonics tests, respectively (p<0.001). A year later effects persist, but math gains dissipate to 0.15 (p=0.06) and phonics to 0.13 (p=0.12). Effects appear to be mediated largely through instructional support by parents and not other parent investment mechanisms, such as more positive parent-child interactions or additional time spent on education at home beyond the intervention. Our results show that parents can be effective conduits of educational instruction even in low-resource settings.

Dynamic Prediction Of Disease Progression With Longitudinal Data, Wenhao Li

Dissertations and Theses (Open Access)

Dynamic prediction plays a pivotal role in clinical research, especially when forecasting time-to-event outcomes based on evolving longitudinal data. This process often leverages the integration of longitudinal and time-to-event data through joint modeling, a prevalent technique. Alongside joint modeling, landmark modeling stands as another key approach in the realm of longitudinal studies. These methodologies are instrumental in dynamically predicting clinical events by utilizing predictor variables measured over time, up until the moment predictions are made. Within this framework, Chapter 2 addresses the challenge of comparing joint modeling and landmark modeling for dynamic prediction in longitudinal studies, introducing a novel algorithm …

Gender-Specific Mental Health Outcomes In Central America: A Natural Experiment, Thea Nagasuru

Computer Science Summer Fellows

While COVID lockdown measures have had varying effects on the mental health of different demographics, several bodies of research have noted their disparate effect on women. Why is women's mental health more negatively impacted by lockdown measures, and how much more are they impacted than men? How can we predict and mitigate these negative effects on women? This paper aims to contribute to answering those questions by comparing COVID stringency measures and their effect on the gap in depression rates between men and women in two neighboring countries: Nicaragua and Honduras.

Stock Market Volatility In The United Kingdom: Simulating Post-Covid-19 Recovery, Bala A. Dahiru, Mohammed Shuaibu, Najibullah Hassanov

CBN Journal of Applied Statistics (JAS)

This paper investigates the time it would take for the FTSE-100 index to reach its post-COVID-19 peak. The paper utilises an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model that accounts for leverage effect and asymmetries. The preferred models amongst competing variants was the Autoregressive Moving Average (ARMA)-EGARCH(2,1) specification and was used to predict daily FTSE-100 data from 5th January 2000 to 21st June 2024. The empirical exercise showed that the COVID-19-induced financial crisis negatively affected the United Kingdom’s stock market performance. The results show that the FTSE100 index could reach its post-pandemic peak around 27th August, 2024 (two months after …

Impact Of Fiscal Policy On Financial Inclusion And Development In Nigeria, Okwanya Innocent, Taiwo A. Olusegun, Aimua E. Peace

CBN Journal of Applied Statistics (JAS)

This paper examines the effect of fiscal policy on financial inclusion and development in Nigeria. The study employs the Autoregressive Distributed Lag (ARDL) model and impulse response function (IRF) to determine the extent and response of financial inclusion and development to fiscal policy changes in Nigeria. The study derives a financial inclusion index from three core indicators: access, usage and quality of financial services, while financial development is measured as the ratio of money supply to GDP (M2/GDP). The results show that government expenditure has a significant positive effect on financial inclusion and development, while tax revenue exerts a negative …

A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte

SMU Data Science Review

Current nonlinear time series methods such as neural networks forecast well. However, they act as a black box and are difficult to interpret, leaving the researchers and the audience with little insight into why the forecasts are the way they are. There is a need for a method that forecasts accurately while also being easy to interpret. This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression. The final method relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the …

Reevaluating Texas Energy Market Forecasts In The Wake Of Recent Extreme Weather Events, Robert A. Derner, Richard W. Butler Ii, Alexandria Neff, Adam R. Ruthford

SMU Data Science Review

This paper provides updated forecasts of energy demand in Texas and recognizes the impact of sustainable energy. It is important that the forecasts of the adoption of sustainable energy are reexamined after Winter Storm Uri crippled the Texas power grid and left many without power. This storm highlighted the issues the Texas power grid had and has continued to struggle with in supplying the state with energy. This paper will offer an overview of the relevant literature on the adoption of sustainable energy and relevant events that have occurred in the state of Texas that will give the reader the …

Modeling Human Temporal Eeg Responses To Vr Visual Stimuli, Richard R. Foster, Connor Delaney, Dean J. Krusienski, Cheng Ly

Biology and Medicine Through Mathematics Conference

No abstract provided.