Physical Sciences and Mathematics Commons^™

A Spectral Adjustment For Spatial Confounding, Yawen Guan, Garritt L. Page, Brian J. Reich, Massimo Ventrucci, Shu Yang

Department of Statistics: Faculty Publications

Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. In this paper, we derive necessary conditions on the coherence between the treatment variable of interest and the unmeasured confounder that ensure the causal effect of the treatment is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. The key assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We show that this assumption in the spectral domain is …

The Local Stability Of A Modified Multi-Strain Sir Model For Emerging Viral Strains, Miguel Fudolig, Reka Howard

Department of Statistics: Faculty Publications

We study a novel multi-strain SIR epidemic model with selective immunity by vaccination. A newer strain is made to emerge in the population when a preexisting strain has reached equilbrium. We assume that this newer strain does not exhibit cross-immunity with the original strain, hence those who are vaccinated and recovered from the original strain become susceptible to the newer strain. Recent events involving the COVID-19 virus shows that it is possible for a viral strain to emerge from a population at a time when the influenza virus, a well-known virus with a vaccine readily available, is active in a …

Interval Estimation Of Proportion Of Second-Level Variance In Multi-Level Modeling, Steven Svoboda

The Nebraska Educator: A Student-Led Journal

Physical, behavioral and psychological research questions often relate to hierarchical data systems. Examples of hierarchical data systems include repeated measures of students nested within classrooms, nested within schools and employees nested within supervisors, nested within organizations. Applied researchers studying hierarchical data structures should have an estimate of the intraclass correlation coefficient (ICC) for every nested level in their analyses because ignoring even relatively small amounts of interdependence is known to inflate Type I error rate in single-level models. Traditionally, researchers rely upon the ICC as a point estimate of the amount of interdependency in their data. Recent methods utilizing an …

Cost Effectiveness Of Sample Pooling To Test For Sars-Cov-2, Baha Abdalhamid, Christopher Richard Bilder, Jodi Louise Garrett, Peter Charles Iwen

Department of Statistics: Faculty Publications

No abstract provided.

Statistical Methodology To Establish A Benchmark For Evaluating Antimicrobial Resistance Genes Through Real Time Pcr Assay, Enakshy Dutta

Department of Statistics: Dissertations, Theses, and Student Work

Novel diagnostic tests are usually compared with gold standard tests for evaluating diagnostic accuracy. For assessing antimicrobial resistance (AMR) to bovine respiratory disease (BRD) pathogens, phenotypic broth microdilution method is used as gold standard (GS). The objective of the thesis is to evaluate the optimal cycle threshold (Ct) generated by real-time polymerase chain reaction (rtPCR) to genes that confer resistance that will translate to the phenotypic classification of AMR. Data from two different methodologies are assessed to identify Ct that will discriminate between resistance (R) and susceptibility (S). First, the receiver operating characteristic (ROC) curve was used to determine the …

Co-Authorship Visualization Of Research On Covid-19 From Web Of Science Data Using Bibliometric Analysis, Akbar Iskandar, Firman Azis, Riskha Dora Candra Dewi, R. Rusli, Ansari Saleh Ahmar

Library Philosophy and Practice (e-journal)

Bibliometric analysis is one of the research approaches that utilizes quantitative and mathematical data to address problems posed in the context of visualization to see patterns in the field of science. In fact, bibliometric analysis may also include a wider overview of the names of the most influential writers in the area of science. This data analysis would discuss the co-authorship of COVID-19 research covering author productivity and author collaboration. The data was collected on 11th May 2020 of Web of Science (WoS) Core Collection database. The literature review was conducted using the keyword: TOPIC: ("covid") AND YEAR PUBLISHED: (2020). …

Using Stability To Select A Shrinkage Method, Dean Dustin

Department of Statistics: Dissertations, Theses, and Student Work

Shrinkage methods are estimation techniques based on optimizing expressions to find which variables to include in an analysis, typically a linear regression. The general form of these expressions is the sum of an empirical risk plus a complexity penalty based on the number of parameters. Many shrinkage methods are known to satisfy an ‘oracle’ property meaning that asymptotically they select the correct variables and estimate their coefficients efficiently. In Section 1.2, we show oracle properties in two general settings. The first uses a log likelihood in place of the empirical risk and allows a general class of penalties. The second …

The Impact Of Pev User Charging Behavior In Building Public Charging Infrastructure, Ahmad Almaghrebi

Durham School of Architectural Engineering and Construction: Dissertations, Thesis, and Student Research

Plug-in electric vehicles (PEVs) play a significant role in the development of green cities since they generate less pollution than conventional vehicles. To promote PEV adoption and mitigate range anxiety, charging infrastructure should be deployed at strategic locations that are readily accessible to the public. Nebraska is working on the expansion of charging infrastructure around the state; however, stakeholders face several difficulties in trying to minimize irregular charging behaviors. Most electric vehicle users plug in and leave their vehicles for an extended time at public parking lots designated for PEVs. Some users even leave their vehicles for longer than 24 …

Community Impact On The Home Advantage Within Ncaa Men's Basketball, Erin O'Donnell

Department of Statistics: Dissertations, Theses, and Student Work

The home advantage is a commonly accepted truth throughout sports performances. This paper investigates the magnitude of the home advantage among NCAA Men’s Basketball teams. It will then look to draw relationships between the magnitude of the home advantage and community aspects such as attendance, location, past program success, and social media presence. Univariate and Multivariate models will be investigated.

Advisor: Walter S Stroup

Exact Distribution Of Linkage Disequilibrium In The Presence Of Mutation, Selection, Or Minor Allele Frequency Filtering, Jiayi Qu, Stephen D. Kachman, Dorian Garrick, Rohan L. Fernando, Hao Cheng

Department of Statistics: Faculty Publications

Linkage disequilibrium (LD), often expressed in terms of the squared correlation (r2) between allelic values at two loci, is an important concept in many branches of genetics and genomics. Genetic drift and recombination have opposite effects on LD, and thus r2 will keep changing until the effects of these two forces are counterbalanced. Several approximations have been used to determine the expected value of r2 at equilibrium in the presence or absence of mutation. In this paper, we propose a probability-based approach to compute the exact distribution of allele frequencies at two loci in a finite population at any generation …

Group Testing Identification: Objective Functions, Implementation, And Multiplex Assays, Brianna D. Hitt

Department of Statistics: Dissertations, Theses, and Student Work

Group testing is the process of combining items into groups to test for a binary characteristic. One of its most widely used applications is infectious disease testing. In this context, specimens (e.g., blood, urine) are amalgamated into groups and tested. For groups that test positive, there are many algorithmic retesting procedures available to identify positive individuals. The appeal of group testing is that the overall number of tests needed is significantly less than for individual testing when disease prevalence is small and an appropriate algorithm is chosen. Group testing has a number of applications beyond infectious disease testing, such as …

Association Between Baseline Abundance Of Peptoniphilus, A Gram-Positive Anaerobic Coccus, And Wound Healing Outcomes Of Dfus, Kyung R. Min, Adriana Galvis, Katherine L. Baquerizo Nole, Rohita Sinha, Jennifer Clarke, Robert S. Kirsner, Dragana Ajdic

Department of Statistics: Faculty Publications

Diabetic foot ulcers (DFUs) lead to nearly 100,000 lower limb amputations annually in the United States. DFUs are colonized by complex microbial communities, and infection is one of the most common reasons for diabetes-related hospitalizations and amputations. In this study, we examined how DFU microbiomes respond to initial sharp debridement and off- loading and how the initial composition associates with 4 week healing outcomes. We employed 16S rRNA next generation sequencing to perform microbial profiling on 50 sam- ples collected from 10 patients with vascularized neuropathic DFUs. Debrided wound sam- ples were obtained at initial visit and after one week …

Representation Of Features As Images With Neighborhood Dependencies For Compatibility With Convolutional Neural Networks, Omid Bazgir, Ruibo Zhang, Saugato Rahman Dhruba, Raziur Rahman, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated …

Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh

Department of Statistics: Faculty Publications

Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist …

In Praise Of Partially Interpretable Predictors, Tri Le, Bertrand S. Clarke

Department of Statistics: Faculty Publications

Often there is an uninterpretable model that is statistically as good as, if not better than, a successful interpretable model. Accordingly, if one restricts attention to interpretable models, then one may sacrifice predictive power or other desirable properties. A minimal condition for an interpretable, usually parametric, model to be better than another model is that the first should have smallermean-squared error or integratedmean-squared error.We show through a series of examples that this is often not the case and give the asymptotic forms of a variety of interpretable, partially interpretable, and noninterpretable methods. We find techniques that combine aspects of both …

Tumor Ablation Due To Inhomogeneous Anisotropic Diffusion In Generic Three-Dimensional Topologies, Erdi Kara, Aminur Rahman, Eugenio Aulisa, Souparno Ghosh

Department of Statistics: Faculty Publications

In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal …

Statistical Downscaling With Spatial Misalignment: Application To Wildland Fire Pm2.5 Concentration Forecasting, Suman Majumder, Yawen Guan, Brian J. Reich, Susan O’Neill, Ana G. Rappold

Department of Statistics: Faculty Publications

Fine particulate matter, PM_2.5, has been documented to have adverse health effects, and wildland fires are a major contributor to PM_2.5 air pollution in the USA. Forecasters use numerical models to predict PM_2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and …

The Role Of Topography, Soil, And Remotely Sensed Vegetation Condition Towards Predicting Crop Yield, Trenton E. Franz, Sayli Pokal, Justin P. Gibson, Yuzhen Zhou, Hamed Gholizadeh, Fatima Amor Tenorio, Daran Rudnick, Derek M. Heeren, Matthew F. Mccabe, Matteo Ziliani, Zhenong Jin, Kaiyu Guan, Ming Pan, John Gates, Brian Wardlow

School of Natural Resources: Faculty Publications

Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what …