Other Statistics and Probability Commons^™

A Copula-Based Framework For Multivariate Count Time Series With Mixed Marginal Distributions, Dimuthu Fernando, Yuxin Wen, Wimarsha Jayanetti

Engineering Faculty Articles and Research

We developed a class of multivariate integer-valued time series models using copula theory. Each count time series is modeled as a Markov chain, with serial dependence characterized through copula-based transition probabilities for Poisson and negative binomial marginals. Cross-sectional dependence is modeled via a trivariate Gaussian or a “t-copula”, allowing for both positive and negative correlations and providing a flexible dependence structure. Model parameters are estimated using likelihood-based inference, where the trivariate Gaussian or t-copula integrals are evaluated through standard randomized Monte Carlo methods. Simulation results, along with an analysis of annual counts of major hurricanes (Category 3+) across the North …

Bayesball : A Comprehensive Framework For Predicting Ucl Injury, Brady M. Pinter, Will Best Ph.D.

SPARK Symposium Presentations

Ulnar Collateral Ligament (UCL) reconstruction, commonly referred to as Tommy John Surgery, has seen a significant rise among Major League Baseball (MLB) pitchers, prompting growing interest in identifying the mechanical and performance-based factors that contribute to injury risk. While previous studies have examined these relationships using traditional frequentist approaches separately, this study combines multiple different model techniques to present a broad framework for finding significant predictors of UCL Surgery. These models include Lasso and Ridge Regression,  Principal Component Regression (PCR) , Partial Least Squares Regression (PLS) , Random Forest, Multiple Linear Regression, and a Bayesian Statistical Model. Using these models, …

An Analytical Prior Selection Procedure For Empirical Bayesian Analysis Using Resampling Techniques: A Simulation-Based Approach Using The Pancreatic Adenocarcinoma Data From The Seer Database, Aditya Chakraborty, Mohan D. Pant

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Introduction: In the field of medical research, empirical Bayesian analysis has emerged as an increasingly applicable approach. This statistical framework offers greater flexibility, enabling researchers to incorporate prior information and rigorously estimate parameters of interest. However, the selection of suitable prior distributions can be a challenging endeavor, with profound implications for the resulting inferences. To address this challenge, this study proposes a new analytical procedure that leverages resampling techniques to guide the choice of priors in Bayesian analysis. Subject and Methods: The study group consisted of patients who had been diagnosed and had died of pancreatic adenocarcinoma (cause-specific death) who …

Meditation In Qualitative Research For Bracketing And Beyond, Katalin Grajzel

Research Methods and Statistics: Graduate Student Scholarship

In this study, I recounted my experience using mantra meditation during a phenomenological study for the purposes of bracketing. The efficacy and purpose of bracketing have been debated from Husserl (1931), whose aimed was to achieve objectivity, to Heidegger (1962) who advocated for immersion of the researcher, through the French school (Merleau-Ponty, 1964) of middle ground, by whom bracketing was seen as the process to unearth and suspend biases for the better understanding of participants’ experiences (Arsel, 2017; Creswell & Creswell, 2017; Creswell & Poth, 2016; Fischer & Guzel, 2023). In this study, however, I propose another approach to bracketing …

The Economic And Environmental Impact Of Shinkansen And High-Speed Rail Infrastructure: A Comparative Analysis Of Economic Growth And Carbon Emissions Reduction, Dillan R. Victory

SPARK Symposium Presentations

The development of Japan's high-speed rail system, the Shinkansen, has played a pivotal role in the country's post-war economic resurgence. Introduced in 1964 with the Tokaidō Shinkansen, this transformative infrastructure investment significantly reduced travel times, bolstered economic activity around station hubs, and facilitated regional development by enabling urban decentralization. This paper explores the long-term economic benefits of high-speed rail, including its impact on land value, business expansion, and carbon emissions. The case study of the Linear Chuo Shinkansen, Japan's latest maglev project, underscores both the economic promise and the political resistance to expansion, particularly in regions such as Shizuoka.

Using …

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.

Topological Regression As An Interpretable And Efficient Tool For Quantitative Structureactivity Relationship Modeling, Ruibo Zhang, Daniel Nolte, Cesar Sanchez-Villalobos, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Quantitative structure-activity relationship (QSAR)modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application inmolecular design.We propose a similaritybased regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learningbased QSAR models and provide better intuitive interpretation by extracting an approximate …

Bagging Improves The Performance Of Deep Learning-Based Semantic Segmentation With Limited Labeled Images: A Case Study Of Crop Segmentation For High-Throughput Plant Phenotyping, Yinglun Zhan, Yuzhen Zhou, Geng Bai, Yufeng Ge

Department of Statistics: Faculty Publications

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation—identifying crops from the background—crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the …

Artificial Intelligence Could Probably Write This Essay Better Than Me, Claire Martino

Augustana Center for the Study of Ethics Essay Contest

No abstract provided.

Editorial: Ipps 2022 - Plant Phenotyping For A Sustainable Future, Elias Kaiser, Philipp Von Gillhaussen, Jennifer Clarke, Ulrich Schurr

Department of Statistics: Faculty Publications

Plants are a venue for addressing the challenges facing humanity. The need for a reliable supply of food, feed, materials, chemicals and energy as well as ways to manage agroecology and climate change are among the challenges that we can address through the sustainable use of plants and plant ecosystems. The research community needs to integrate plant systems approaches, from molecular to organismal to applications in the field and ecosystems, to increase productivity sustainably while using fewer land, water, and nutrient resources. In the past two decades, plant phenotyping research has developed a highly valuable portfolio of technologies, processes and …

Discussion On “Spatial+: A Novel Approach To Spatial Confounding” By Dupont, Wood, And Augustin, Brian J. Reich, Shu Yang, Yawen Guan

Department of Statistics: Faculty Publications

Congratulations to the authors for this thoughtful and timely contribution to the spatial confounding literature. The intuitive nature of the method and simplicity of the estimation procedure will surely make Spatial+ popular with practitioners, and the theoretical developments are a major advance for researchers in this area. There is much to discuss! We have formatted our discussion in two sections: in Section 2 we consider the assumptions and statistical properties of Spatial+, and in Section 3 we examine how Spatial+ fits in the wider literature on spatial causal inference.

Prebiotic Proanthocyanidins Inhibit Bile Reflux–Induced Esophageal Adenocarcinoma Through Reshaping The Gut Microbiome And Esophageal Metabolome, Katherine M. Weh, Connor L. Howard, Yun Zhang, Bridget A. Tripp, Jennifer Clarke, Amy B. Howell, Joel H. Rubenstein, Julian A. Abrams, Maria Westerhoff, Laura A. Kresty

Department of Statistics: Faculty Publications

The gut and local esophageal microbiome progressively shift from healthy commensal bacteria to inflammation-linked pathogenic bacteria in patients with gastroesophageal reflux disease, Barrett’s esophagus, and esophageal adenocarcinoma (EAC). However, mechanisms by which microbial communities and metabolites contribute to reflux-driven EAC remain incompletely understood and challenging to target. Herein, we utilized a rat reflux-induced EAC model to investigate targeting the gut microbiome–esophageal metabolome axis with cranberry proanthocyanidins (C-PAC) to inhibit EAC progression. Sprague-Dawley rats, with or without reflux induction, received water or C-PAC ad libitum (700 μg/rat/day) for 25 or 40 weeks. C-PAC exerted prebiotic activity abrogating reflux-induced dysbiosis and mitigating …

Mapping Urban Form Into Local Climate Zones For The Continental Us From 1986–2020, Meng Qi, Chunxue Xu, Wenwen Zhang, Matthias Demuzere, Perry Hystad, Tianjun Lu, Peter James, Benjamin Bechtel, Steve Hankey

Earth and Environmental Sciences Faculty Publications

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people’s activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling …

Is The Declining Birthrate Really An Issue For The Economy?, Harsh Ramesh Pednekar, Theodore Lee, Darrion Chin

Introduction to Research Methods RSCH 202

This study aims to explore the complex implications of declining birth rates on the economy, focusing on GDP per capita as a crucial metric, and aims to uncover both potential opportunities and challenges stemming from this demographic transformation using regression analysis. Using a quantitative methodology and secondary data from OECD.stat, World Population Review, and World Bank, the study explores the relationship between declining birth rates and economic impacts. GDP per capita serves as an essential dependent variable, and it accounts for control variables such as labour force participation, literacy, and education levels, child dependence ratio, and physical capital. Past studies …

Bingroup2: Statistical Tools For Infection Identification Via Group Testing, Christopher R. Bilder, Brianna D. Hitt, Brad J. Biggerstaff, Joshua M. Tebbs, Christopher S. Mcmahan

Department of Statistics: Faculty Publications

Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm’s operating characteristics, such as the expected number of …

Good Practices And Common Pitfalls In Climate Time Series Changepoint Techniques: A Review, Robert B. Lund, Claudie Beaulieu, Rebecca Killick, Qiqi Lu, Xueheng Shi

Department of Statistics: Faculty Publications

Climate changepoint (homogenization) methods abound today, with a myriad of techniques existing in both the climate and statistics literature. Unfortunately, the appropriate changepoint technique to use remains unclear to many. Further complicating issues, changepoint conclusions are not robust to perturbations in assumptions; for example, allowing for a trend or correlation in the series can drastically change changepoint conclusions. This paper is a review of the topic, with an emphasis on illuminating the models and techniques that allow the scientist to make reliable conclusions. Pitfalls to avoid are demonstrated via actual applications. The discourse begins by narrating the salient statistical features …

Data Sharing And Ontology Use Among Agricultural Genetics, Genomics, And Breeding Databases And Resources Of The Agbiodata Consortium, Jennifer L. Clarke, Laurel D. Cooper, Monica F. Poelchau, Tanya Z. Berardini, Justin Elser, Andrew D. Farmer, Stephen Ficklin, Sunita Kumari, Marie-Angélique Laporte, Rex T. Nelson, Rie Sadohara, Peter Selby, Anne E. Thessen, Brandon Whitehead, Taner Z. Sen

Department of Statistics: Faculty Publications

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as ‘databases’ throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and …

Assessment Of First-Phase Covid-19 Pandemic In Europe Using Hierarchical Clustering Based On Principal Components Analysis, Sanjay Kumar, Evrim Oral

School of Public Health Faculty Publications

It is of great interest for researchers to assess the COVID-19 pandemic in Europe. Grouping of COVID-19-affected regions is an effective way to monitor and optimize planning to combat the disease. This paper applied hierarchical clustering based on principal components analysis (HCPCA) to COVID-19 data from affected European countries. Considering several attribute indices, we obtained a new set of indicators using principal components analysis to aggregate and reduce the dimension of attribute indices of affected countries. Further, we obtained groups of affected countries subject to their similarity using hierarchical clustering to the reduced observations of new attributes indices of these …

Pre-Sleep Feeding, Sleep Quality, And Markers Of Recovery In Division I Ncaa Female Soccer Players, Casey E. Greenwalt, Elisa Angeles, Matthew D. Vukovich, Abbie E. Smith-Ryan, Chris W. Bach, Stacy T. Sims, Tucker Zeleny, Kristen E. Holmes, David M. Presby, Katie J. Schiltz, Marine Dupuit, Liliana I. Renteria, Michael J. Ormsbee

Department of Statistics: Faculty Publications

Pre-sleep nutrition habits in elite female athletes have yet to be evaluated. A retrospective analysis was performed with 14 NCAA Division I female soccer players who wore a WHOOP, Inc. band – a wearable device that quantifies recovery by measuring sleep, activity, and heart rate metrics through actigraphy and photoplethysmography, respectively – 24 h a day for an entire competitive season to measure sleep and recovery. Pre-sleep food consumption data were collected via surveys every 3 days. Average pre-sleep nutritional intake (mean ± sd: kcals 330 ± 284; cho 46.2 ± 40.5 g; pro 7.6 ± 7.3 g; fat 12 …

Increasing Racial Diversity In The North American Plant Phenotyping Network Through Conference Participation Support, David Lebauer, Alexander Bucksch, Jennifer Clarke, Jesse Potts, Sonali Roy

Department of Statistics: Faculty Publications

A key goal of the North American Plant Phenotyping Network (NAPPN) annual conference is to cultivate a new generation of scientists from diverse backgrounds. As part of their effort to diversify the plant phenomics research community, NAPPN acquired funding to cover all attendance costs for participants from historically black colleges and universities (HBCU) for the 2022 annual meeting. Seven award recipients represented the first attendees from HBCUs in the conference’s 6-year history. In this commentary, we report on the impact of the conference awards, including lessons learned, and the future of the award.

Near-Term Effects Of Perennial Grasses On Soil Carbon And Nitrogen In Eastern Nebraska, Salvador Ramirez Ii, Marty R. Schmer, Virginia L. Jin, Robert B. Mitchell, Kent M. Eskridge

Department of Statistics: Faculty Publications

Incorporating native perennial grasses adjacent to annual row crop systems managed on marginal lands can increase system resiliency by diversifying food and energy production. This study evaluated (1) soil organic C (SOC) and total N stocks (TN) under warm-season grass (WSG) monocultures and a low diversity mixture compared to an adjacent no-till continuous-corn system, and (2) WSG total above-ground biomass (AGB) in response to two levels of N fertilization from 2012 to 2017 in eastern Nebraska, USA. The WSG treatments consisted of (1) switchgrass (SWG), (2) big bluestem (BGB), and (3) low-diversity grass mixture (LDM; big bluestem, Indiangrass, and sideoat …

Integrating And Optimizing Genomic, Weather, And Secondary Trait Data For Multiclass Classification, Vamsi Manthena, Diego Jarquín, Reka Howard

Department of Statistics: Faculty Publications

Modern plant breeding programs collect several data types such as weather, images, and secondary or associated traits besides the main trait (e.g., grain yield). Genomic data is high-dimensional and often over-crowds smaller data types when naively combined to explain the response variable. There is a need to develop methods able to effectively combine different data types of differing sizes to improve predictions. Additionally, in the face of changing climate conditions, there is a need to develop methods able to effectively combine weather information with genotype data to predict the performance of lines better. In this work, we develop a novel …

Federated Learning Framework Integrating Refined Cnn And Deep Regression Forests, Daniel Nolte, Omid Bazgir, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Predictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as …

Federated Learning Framework Integrating Refined Cnn And Deep Regression Forests, Daniel Nolte, Omid Bazgir, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Predictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as …

Socioeconomic Factors In The Diagnosis And Treatment Of Malignant Melanoma In Hispanic Vs. Non-Hispanic Patients: A National Cancer Database (Ncdb) Study, Julia Griffin, Sarah J. Aurit, Timothy Malouff, Peter Silberstein

Department of Statistics: Faculty Publications

Background: The incidence of melanoma is rapidly increasing in the United States. There is a paucity of research of how melanoma affects the Hispanic population, the quickest growing population.

Objective: To identify and understand how socioeconomic factors affect a Hispanic patients health outcome and treatment of malignant melanoma with comparisons to white, non-Hispanic (WNH) patients.

Methods: A retrospective study utilizing the National Cancer Database (NCDB) was completed investigating Hispanic patients (n=2282) and WNH patients (n=190,469) with Stage I-IV malignant melanoma. Outcome and socioeconomic variables were identified and compared across groups. Data was analyzed with SPSS and SAS …

Estimating The Prevalence Of Two Or More Diseases Using Outcomes From Multiplex Group Testing, Md S. Warasi, Joshua M. Tebbs, Christopher S. Mcmahan, Christopher R. Bilder

Department of Statistics: Faculty Publications

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, …

Penguins Go Parallel: A Grammar Of Graphics Framework For Generalized Parallel Coordinate Plots, Susan Vanderplas, Yawei Ge, Antony Unwin, Heike Hofmann

Department of Statistics: Faculty Publications

Parallel Coordinate Plots (PCP) are a valuable tool for exploratory data analysis of high-dimensional numerical data. The use of PCPs is limited when working with categorical variables or a mix of categorical and continuous variables. In this article, we propose Generalized Parallel Coordinate Plots (GPCP) to extend the ability of PCPs from just numeric variables to dealing seamlessly with a mix of categorical and numeric variables in a single plot. In this process we find that existing solutions for categorical values only, such as hammock plots or parsets become edge cases in the new framework. By focusing on individual observations …

Viscoelastic Properties Of Human Facial Skin And Comparisons With Facial Prosthetic Elastomers, Mark W. Beatty, Alvin G. Wee, D. B. Marx, Lauren Ridgway, Bobby Simetich, Thiago Carvalho De Sousa, Kevin Vakilzadian, Joel Schulte

Department of Statistics: Faculty Publications

Prosthesis discomfort and a lack of skin-like quality is a source of patient dissatisfaction with facial prostheses. To engineer skin-like replacements, knowledge of the differences between facial skin properties and those for prosthetic materials is essential. This project measured six viscoelastic properties (percent laxity, stiffness, elastic deformation, creep, absorbed energy, and percent elasticity) at six facial locations with a suction device in a human adult population equally stratified for age, sex, and race. The same properties were measured for eight facial prosthetic elastomers currently available for clinical usage. The results showed that the prosthetic materials were 1.8 to 6.4 times …

Early Detection Of Covid-19 In Female Athletes Using Wearable Technology, Liliana I. Rentería, Casey E. Greenwalt, Sarah Johnson, Shiloah Shiloah Kviatkovsky, Marine Dupuit, Elisa Angeles, Sachin Narayanan, Tucker Zeleny, Michael J. Ormsbee

Department of Statistics: Faculty Publications

Background: Heart rate variability (HRV), respiratory rate (RR), and resting heart rate (RHR) are common variables measured by wrist-worn activity trackers to monitor health, fitness, and recovery in athletes. Variations in RR are observed in lower-respiratory infections, and preliminary data suggest changes in HRV and RR are linked to early detection of COVID-19 infection in nonathletes.

Hypothesis: Wearable technology measuring HRV, RR, RHR, and recovery will be successful for early detection of COVID-19 in NCAA Division I female athletes.

Study Design: Cohort study.

Level of Evidence: Level 2.

Methods: Female athletes wore WHOOP, Inc. bands …

Socio‑Economic Inequalities In Minimum Dietary Diversity Among Bangladeshi Children Aged 6–23 Months: A Decomposition Analysis, Satyajit Kundu, Pranta Das, Ashfikur Rahman, Hasan Al Banna, Kaniz Fatema, Akhtarul Islam, Shobhit Srivastava, T. Muhammad, Rakhi Dey, Ahmed Hossain

Department of Statistics: Faculty Publications

This study aimed to measure the socio-economic inequalities in having minimum dietary diversity (MDD) among Bangladeshi children aged 6–23 months as well as to determine the factors that potentially contribute to the inequity. The Bangladesh Demographic and Health Survey (BDHS) 2017–2018 data were used in this study. A sample of 2405 (weighted) children aged 6–23 months was included. The overall weighted prevalence of MDD was 37.47%. The concentration index (CIX) value for inequalities in MDD due to wealth status was positive and the concentration curve lay below the line of equality (CIX: 0.1211, p < 0.001), where 49.47% inequality was contributed by wealth status, 25.06% contributed by the education level of mother, and 20.41% contributed by the number of ante-natal care (ANC) visits. Similarly, the CIX value due to the education level of mothers was also positive and the concentration curve lay below the line of equality (CIX: 0.1341, p < 0.001), where 52.68% inequality was contributed by the education level of mother, 18.07% contributed by wealth status, and 14.69% contributed by the number of ANC visits. MDD was higher among higher socioeconomic status (SES) groups. Appropriate intervention design should prioritize minimizing socioeconomic inequities in MDD, especially targeting the contributing factors of these inequities.