Statistics and Probability Commons^™

Statistical Methods In Research, Horahenage Dixon Vimalajeewa

UNL Faculty Course Portfolios

This course portfolio documents the design, delivery, assessment, student-learning evidence, and reflective evaluation of STAT 801A-700: Statistical Methods in Research, an online asynchronous service course designed to introduce students from diverse disciplinary backgrounds to foundational statistical reasoning and applied data analysis. The course is a non-calculus-based introduction to statistical methods used to answer research questions, with emphasis on collecting, organizing, describing, analyzing, and drawing conclusions from data. The course also emphasizes applications relevant to biology, agriculture, and other research-oriented fields. This is an online distance course aimed to develop students’ understanding of basic probability and statistical concepts, recognize the importance …

A Temporal Extension Of Tasselnet With Uncertainty Quantification Using Bayesian Latent Autoregressive Model, Gayara Demini Fernando Muthunama Gonnage

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Manual counting of maize tassels remains a time-consuming and labor-intensive process. Automating tassel counting using maize field images has received significant interest, with researchers exploring methods to streamline this task. Accurate tassel detection can substantially reduce both time and labor costs, offering a practical solution for large-scale agricultural management. Current studies in this area predominantly focus on training object detection algorithms to enhance prediction accuracy and efficiency. Images for this task are often captured in sequences, meaning they capture the exact location of the maize fields over time. However, existing tassel detection and counting methods typically ignore this sequential nature, …

Changepoint Detection As Model Selection: A General Framework, Michael A. Grantham

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation presents a general framework for changepoint detection based on ℓ₀ model selection. The core method, Iteratively Reweighted Fused Lasso (IRFL), improves upon the generalized lasso by adaptively reweighting penalties to enhance support recovery and minimize criteria such as the Bayesian Information Criterion (BIC). The approach allows for flexible modeling of seasonal patterns, linear and quadratic trends, and autoregressive dependence in the presence of changepoints.

Simulation studies demonstrate that IRFL achieves accurate changepoint detection across a wide range of challenging scenarios, including those involving nuisance factors such as trends, seasonal patterns, and serially correlated errors. The framework is …

On Bayesian Empirical Likelihood-Based Method For Complex Survey Data With Application To Non-Probability Sampling, Md Hasibur Rahman

Department of Statistics: Dissertations, Theses, and Student Research

This thesis develops a Bayesian empirical likelihood (BEL) framework for inference under complex survey designs and extends it to non-probability sampling. Parametric likelihood based methods are difficult to apply to complex survey data because the likelihood is rarely available in closed form. EL provides a flexible alternative by replacing the parametric likelihood with an empirical likelihood constructed from moment conditions. The proposed method first integrates empirical likelihood constraints with survey design features then extends BEL to non-probability sampling through selection models and design consistent restrictions. Posterior inference is carried out using a Metropolis–Hastings MCMC algorithm. A real-data analysis further illustrates …

Detection Of Activity Cliffs Produced By Anti-Cancer Drugs And An Algorithm For Reliable Predictions In Affected Areas, Sarah Josephine Aurit

Department of Statistics: Dissertations, Theses, and Student Research

An activity cliff (AC) occurs when drugs close in chemical space produce dissimilar biological results. We focus on developing an inferential procedure to detect the presence of ACs in a chemical landscape. If detected, we provide a distance-based procedure that can be used to identify regions of stability in the chemical landscape of interest and generate prediction with higher precision in those areas of stability. We conceptualize the chemical landscape as a spatial random field and use spatial models for prediction of efficacy for new drugs based on “distance” in chemical space. We argue that an AC manifests itself by …

Online Prediction Of Streaming Data, Aleena Chanda

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

We present two new approaches for point prediction with streaming data based on a) the Count-Min sketch and b) Gaussian Process Priors with random bias. The methods are intended for the most general case where no true model can be usefully formulated for the data stream. In statistical contexts, this is often called the M open problem class. For the Count Min Sketch method we show that the predicted distribution function ^F converges to F under the assumption that the data consists of i.i.d samples from a fixed distribution function F. To implement the Gaussian Process Prior methods, we used …

Multivariate Mixture Regression Models With Known Group Membership And Informative Priors, Pahalapathirage Dona Kalani Hasanthika

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

We introduced couple different novel approaches to incorporate latent variable information to multivariate mixture regression models with both Gaussian and count data. We also evaluated the performance of these models with existing best approaches with simulated data from various sampling structures and also evaluated one of the models performance with rice metabolite data that provided some novel insights as well as validating existing literature about performance and behavior of these metabolites. We validated the method using extensive simulations and a real-world application. In both quantitative covariate designs and complex treatment design simulations, our method consistently outperformed established tools like limma, …

Leveraging Historical Data For Estimating Genetic Gain And Implementing Genomic Selection In A Student Led Barley Breeding Program, Sydney Graham

Department of Statistics: Dissertations, Theses, and Student Research

In Nebraska, winter feed barley presents an emerging market for producers and an opportunity to diversify cropping systems. The University of Nebraska Barley Breeding Program aims to develop high-yielding, winter-hardy varieties. A unique aspect of this program is that doctoral students serve as barley breeders and are responsible for crossing, data collection, and advancement decisions. While this provides hands-on experience for the students, the impact of student leadership has not been examined.

This study used a historical data set to evaluate the realized genetic gain of the breeding program, and as a training population for genomic selection. The dataset consisted …

Group And State-Specific Estimation In Animal Movement Models Using A Bayesian Approach, Anita Bhandari Sharma

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This research study investigates statistical approaches for modeling the movement patterns of white-tailed deer in Louisiana using GPS tracking data. We start by classifying the latent behavioral states using a hidden Markov model (HMM) and then integrate those inferred states into a state-dependent step selection framework to evaluate the land cover preferences. Standard HMMs, however, assume the same movement patterns for all animals, overlooking the differences due to characteristics such as sex, age, breeding season, etc.

To address this limitation, we extend the modeling framework to incorporate the group-level structure defined by similar characteristics or conditions to assess whether such …

Variable Selection In Distance Metric Learning And Triplet Constraints For Deep Learning Based Ordinal Classification, James D. Clothier

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The purpose of this research is to augment linear and kernelized ordinal distance metric learning (L/KODML) techniques with a proposed variable selection methodology that integrates the Sequential Multi-Response Feature Selection (SMuRFS) algorithm. Additionally, we aim to embed ordinal triplet constraints into a deep learning architecture, and to propose a general framework for deep learning-based ordinal classification. A variety of simulation studies and real data experiments were conducted to evaluate the various methodologies. For the distance metric learning and variable selection, results showed that the integration of SMuRFS performed effective variable selection and improved prediction accuracy. For the triplet constraints, incorporating …

Performance Of Acoustic Telemetry And Space Use Of Pallid Sturgeon In The Lower Platte River, Nebraska, Christopher F. Pullano

School of Natural Resources: Dissertations, Theses, and Student Research

Pallid Sturgeon (Scaphirhynchus albus) are centenarian, potamodromous, rheophiles that historically occupied the Missouri River and Mississippi River basins. Listed on the U.S. Endangered Species Act in 1990, population declines are attributed to habitat fragmentation and degradation, as well as overharvest, and hybridization. A knowledge gap exists regarding the extent to which tributaries facilitate key life stages for Pallid Sturgeon. This study evaluated the capacity of acoustic telemetry to monitor the movements of Pallid Sturgeon in a shallow, braided tributary to the Missouri River. The specific objectives were to (1) evaluate the environmental variables influencing the performance of acoustic …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Design And Evaluation Of An Esa-Based Method Of Ensemble Subsetting For A Wofs (Warn On Forecast-Like System), Daniel J. Butler

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Forecasting severe thunderstorm environments in the southeastern United States can be challenging due to mesoscale heterogeneities such as shortwave troughs, pre-existing airmass boundaries, cold fronts aloft, low-level jets, dry air intrusions, and mesoscale lows. To combat these challenges, ensemble sensitivity analysis (ESA) may be applied to a Warn-on-Forecast (WOF)-like ensemble to improve forecasts of severe convection through ensemble weighting and subsetting. Ensemble-based weighting and subsetting uses ensemble members that most accurately represent the thunderstorm environment in areas of mesoscale heterogeneity. This study creates and evaluates the ensemble-based weighting and subsetting in four cases of severe thunderstorm occurrence. The open parameter …

Phylogeny And Disparity Of Ammonoid Family Acanthoceratidae Over Ocean Anoxic Event 2, Lindsey Howard

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

The widespread use of genera as proxies for species in paleobiological studies might affect the results of these studies. Although most attention has been given to taxonomic diversity studies, this could also be true of disparity and phylogenetic studies. In particular, the assumption that particular character states truly diagnose all members of a genus might distort results. This study examines the disparity of Acanthoceratid ammonoids at both the generic and species level. 149 species from 42 genera were examined with 52 characters measured. Following the measurements, an inverse modeling simulation was run 100 times to generate a simulated phylogeny with …

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 …

Morphometric Analysis And Taxonomic Re-Evaluation Of Pepsis Cerberus Lucas And P. Elegans Lepeletier (Hymenoptera: Pompilidae: Pepsinae: Pepsini), Frank E. Kurczewski, Akira Shimizu, Diane H. Kiernan

Insecta Mundi

Hurd (1952) separated Pepsis cerberus Lucas from P. elegans Lepeletier (Hymenoptera: Pompilidae: Pepsinae: Pepsini) based on external morphology and biogeography. Vardy (2005) synonymized the familiar and historically well-documented P. cerberus and P. elegans, combining these Nearctic taxa with several Neotropical variants in an extremely broad definition of P. menechma Lepeletier. In doing so, Vardy (2005) breached the principle of nomenclatural stability. He ignored the prevailing usage and clearly violated articles 23.2, 23.3 and 23.9.1.2 of the ICZN (1999). Morphological differences, ecological divergence, and narrow sympatric geographic distribution of P. cerberus and P. elegans …

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 …

Detection Of Deficiencies And Data Analysis Of Bridge Members With Deep Convolutional Neural Networks, Bennett Jackson

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Concrete cracks and structural steel corrosion are two of the most common defects in bridges. Quantifying and classifying these defects provide bridge inspectors and engineers with valuable data for assessing deterioration levels. However, the bridge inspection process is typically a subjective, time intensive, and tedious task, as defects can be overlooked or in locations not easily accessible. Previous studies have investigated deep learning-based inspection methods, implementing popular models such as Mask R-CNN and U-Net. The architectures of these models offer certain advantages depending on the required task. This thesis aims to evaluate and compare Mask R-CNN and U-Net regarding their …

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 …

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 …

Statistical And Machine Learning Approaches To Describe Factors Affecting Preweaning Mortality Of Piglets, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the United States Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, …

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 …

A Classical Fall Statistics Problem, Timothy Meyer

Cornhusker Economics

An evaluation of traditional baseball measures and suggestions for alternatives, centering on statistics related to the offensive quality of a player.

Exploring Experimental Design And Multivariate Analysis Techniques For Evaluating Community Structure Of Bacteria In Microbiome Data, Kelsey Karnik

Department of Statistics: Dissertations, Theses, and Student Research

The gut microbiome plays a crucial role in human health, and by working collaboratively with microbiologists, we aim to further our understanding of the human gut and its impact on human health. Promoting a diverse microbiome is emphasized throughout microbiology literature, and involving a statistician in designing experiments to relate gut bacteria and some measured health outcome is crucial for ensuring valid and accurate results. By adopting new experimental design and analysis methods, researchers can begin to gain a deeper understanding of how the genetics of our food affect the composition of taxa within the gut microbiome. This dissertation is …

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 …