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Reply To Response By Fbi Laboratory Filed In Illinois V. Winfield And Affidavit By Biederman Et Al. (2022) Filed In Us V. Kaevon Sutton (2018 Cf1 009709), Susan Vanderplas, Kori Khan, Heike Hofmann, Alicia Carriquiry

Department of Statistics: Faculty Publications

1 Preliminaries

1.1 Scope

The aim of this document is to respond to issues raised in Federal Bureau of Investigation¹ and Alex Biedermann, Bruce Budowle & Christophe Champod.²

1.2 Conflict of Interest

We are statisticians employed at public institutions of higher education (Iowa State University and University of Nebraska, Lincoln) and have not been paid for our time or expertise when preparing either this response or the original affidavit.³ We provide this information as a public service and as scientists and researchers in this area.

1.3 Organization

The rest of the document precedes as follows: we begin …

Genomic Prediction Accuracy Of Stripe Rust In Six Spring Wheat Populations By Modeling Genotype By Environment Interaction, Kassa Semagn, Muhammad Iqbal, Diego Jarquin, Harpinder Randhawa, Reem Aboukhaddour, Reka Howard, Izabela Ciechanowska, Momna Farzand, Raman Dhariwal, Colin W. Hiebert, Amidou N’Diaye, Curtis Pozniak, Dean Spaner

Department of Statistics: Faculty Publications

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with …

Comparing Artificial-Intelligence Techniques With State-Of-The-Art Parametric Prediction Models For Predicting Soybean Traits, Susweta Ray, Diego Jarquin, Reka Howard

Department of Statistics: Faculty Publications

Soybean [Glycine max (L.) Merr.] is a significant source of protein and oil and is also widely used as animal feed. Thus, developing lines that are superior in terms of yield, protein, and oil content is important to feed the ever-growing population. As opposed to high-cost phenotyping, genotyping is both cost and time efficient for breeders because evaluating new lines in different environments (location–year combinations) can be costly. Several genomic prediction (GP) methods have been developed to use the marker and environment data effectively to predict the yield or other relevant phenotypic traits of crops. Our study compares a conventional …

Split Classification Model For Complex Clustered Data, Katherine Gerot

Honors Theses

Classification in high-dimensional data has generated tremendous interest in a multitude of fields. Data in higher dimensions often tend to reside in non-Euclidean metric space. This prevents Euclidean-based classification methodologies, such as regression, from reliably modeling the data. Many proposed models rely on computationally-complex embedding to convert the data to a more usable format. Others, namely the Support Vector Machine, rely on kernel manipulation to implicitly describe the "feature space" to arrive at a non-linear decision boundary. The proposed methodology in this paper seeks to classify complex data in a relatively computationally-simple and explainable manner.

Firearms And Toolmark Error Rates, Susan Vanderplas, Kori Khan, Heike Hofmann, Alicia L. Carriquiry

Department of Statistics: Faculty Publications

We have outlined several problems with the state of error rate studies on firearm and toolmark examination. Fundamentally, we do not know what the error rate is for these types of comparisons. This is a failure of the scientific study of toolmarks, rather than the examiners themselves, but until this is corrected with multiple studies that meet the criteria described in Section 3, we cannot support the use of this evidence in criminal proceedings.

A Survey On The Use Of Plastic Versus Biodegradable Bottles For Drinking Water Packaging In The United Arab Emirates, Himadri Rajput, Munjed A. Maraqa, Fatima Zraydi, Lina A. Al Khatib, Noor Ameen, Rime Ben Elkaid, Safia S. Al Jaberi, Noura A. Alharbi, Reka Howard, Ashraf Aly Hassan

Department of Statistics: Faculty Publications

Due to intensive utilization and extensive production, plastic waste is becoming a serious threat to the environment and human health. The situation is even worse in countries such as the United Arab Emirates (UAE), where single-use plastic water bottles add to the load of plastic pollution. The main objective of this survey was to assess the extent of bottled water utilization by the UAE residents and their awareness of the environmental concerns arising from single-use plastic bottles. The aim was also to evaluate their willingness to shift towards using biodegradable plastic bottles. This study involved the feedback of 2589 respondents …

Seasonal Variation In Terrestrial Invertebrate Subsidies To Tropical Streams And Implications For The Feeding Ecology Of Hart’S Rivulus (Anablepsoides Hartii), David C. Owens, Thomas N. Heatherly, Kent M. Eskridge, Colden V. Baxter, Steven A. Thomas

Department of Statistics: Faculty Publications

Terrestrial invertebrates are important subsidies to fish diets, though their seasonal dynamics and importance to tropical stream consumers are particularly understudied. In this year-round study of terrestrial invertebrate input to two Trinidadian headwater streams with different forest canopy densities, we sought to (a) measure the mass and composition of terrestrial inputs with fall-in traps to evaluate the influences of seasonality, canopy cover, and rainfall intensity, and; (b) compare terrestrial and benthic prey importance to Anablepsoides hartii(Hart’s Rivulus), the dominant invertivorous fish in these streams, by concurrently measuring benthic and drifting invertebrate standing stocks and the volume and composition of …

Cranberry Polyphenols In Esophageal Cancer Inhibition: New Insights, Katherine M. Weh, Yun Zhang, Connor L. Howard, Amy B. Howell, Jennifer L. Clarke, Laura A. Kresty

Department of Statistics: Faculty Publications

Esophageal adenocarcinoma (EAC) is a cancer characterized by rapidly rising incidence and poor survival, resulting in the need for new prevention and treatment options. We utilized two cranberry polyphenol extracts, one proanthocyanidin enriched (C-PAC) and a combination of anthocyanins, flavonoids, and glycosides (AFG) to assess inhibitory mechanisms utilizing premalignant Barrett’s esophagus (BE) and EAC derived cell lines. We employed reverse phase protein arrays (RPPA) and Western blots to examine cancer-associated pathways and specific signaling cascades modulated by C-PAC or AFG. Viability results show that C-PAC is more potent than AFG at inducing cell death in BE and EAC cell lines. …

Genome-Wide Association Study Of Disease Resilience Traits From A Natural Polymicrobial Disease Challenge Model In Pigs Identifies The Importance Of The Major Histocompatibility Complex Region, Jian Cheng, Rohan Fernando, Hao Cheng, Stephen D. Kachman, Kyusang Lim, John C.S. Harding, Michael K. Dyck, Frederic Fortin, Graham S. Plastow, Piggen Canada Research Consortium, Jack C.M. Dekkers

Department of Statistics: Faculty Publications

Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of …

Kryging: Geostatistical Analysis Of Large-Scale Datasets Using Krylov Subspace Methods, Suman Majumder, Yawen Guan, Brian J. Reich, Arvind K. Saibaba

Department of Statistics: Faculty Publications

Analyzing massive spatial datasets using a Gaussian process model poses computational challenges. This is a problem prevailing heavily in applications such as environmental modeling, ecology, forestry and environmental health. We present a novel approximate inference methodology that uses profile likelihood and Krylov subspace methods to estimate the spatial covariance parameters and makes spatial predictions with uncertainty quantification for point-referenced spatial data. The proposed method, Kryging, applies for both observations on regular grid and irregularly-spaced observations, and for any Gaussian process with a stationary isotropic (and certain geometrically anisotropic) covariance function, including the popular Matérn covariance family. We make use of …

Comparing Machine Learning Techniques With State-Of-The-Art Parametric Prediction Models For Predicting Soybean Traits, Susweta Ray

Department of Statistics: Dissertations, Theses, and Student Work

Soybean is a significant source of protein and oil, and also widely used as animal feed. Thus, developing lines that are superior in terms of yield, protein and oil content is important to feed the ever-growing population. As opposed to the high-cost phenotyping, genotyping is both cost and time efficient for breeders while evaluating new lines in different environments (location-year combinations) can be costly. Several Genomic prediction (GP) methods have been developed to use the marker and environment data effectively to predict the yield or other relevant phenotypic traits of crops. Our study compares a conventional GP method (GBLUP), a …

Risk-Based Machine Learning Approaches For Probabilistic Transient Stability, Umair Shahzad

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive …

Faculty Versus Student Repeatability On Evaluating Translucency Of The Anterior Dentition, James L. Sheets, David B. Marx, Nina Ariani, Valentim A. R. Barão, Alvin G. Wee

Department of Statistics: Faculty Publications

The objective was to compare the repeatability between dental faculty, whose clinical practice was primarily restorative dentistry, and final year dental students in categorizing the inherent translucency of images selected at random using either a 3- or 7-point scale (translucent to opaque). Digital images of anterior dentition were randomly selected based on inherent translucency. Thirty images (five were repeated) were randomized and categorized by 20 dental students and 20 faculty on their inherent translucency. Statistical analysis was performed using an F test for analysis of variance at 95% confidence interval. A covariance parameter estimate (CPE) was accomplished to compare the …

Incorporating Molecular Markers And Causal Structure Among Traits Using A Smith-Hazel Index And Structural Equation Models, Juan Valente Hidalgo-Contreras, Josafhat Salinas-Ruiz, Kent M. Eskridge, Stephen P. Baenziger

Department of Statistics: Faculty Publications

The goal in breeding programs is to choose candidates that produce offspring with the best phenotypes. In conventional selection, the best candidate is selected with high genotypic values (unobserved), in the assumption that this is related to the observed phenotypic values for several traits. Multi-trait selection indices are used to identify superior genotypes when a number of traits are to be considered simultaneously. Often, the causal relationship among the traits is well known. Structural equation models (SEM) have been used to describe the causal relationships among variables in many biological systems. We present a method for multi-trait genomic selection that …

Posterior Propriety Of An Objective Prior For Generalized Hierarchical Normal Linear Models, Cong Lin, Dongchu Sun, Chengyuan Song

Department of Statistics: Faculty Publications

Bayesian Hierarchical models has been widely used in modern statistical application. To deal with the data having complex structures, we propose a generalized hierarchical normal linear (GHNL) model which accommodates arbitrarily many levels, usual design matrices and ‘vanilla’ covariance matrices. Objective hyperpriors can be employed for the GHNL model to express ignorance or match frequentist properties, yet the common objective Bayesian approaches are infeasible or fraught with danger in hierarchical modelling. To tackle this issue, [Berger, J., Sun, D., & Song, C. (2020b). An objective prior for hyperparameters in normal hierarchical models. Journal of Multivariate Analysis, 178, 104606. https://doi.org/10.1016/j.jmva.2020.104606] …

Factors Influencing Student Outcomes In A Large, Online Simulation-Based Introductory Statistics Course, Ella M. Burnham

Department of Statistics: Dissertations, Theses, and Student Work

The demand for statistical knowledge and skills is growing in many disciplines, so more students are enrolling in introductory statistics courses (Blair, Kirkman, & Maxwell, 2018). At the same time, institutions are seeking course delivery methods that allow for greater flexibility for students, especially following the onset of the COVID-19 pandemic; therefore, there is more interest in the development and delivery of online introductory statistics courses.

To address this, I collaboratively designed an online introductory statistics course which focuses on simulation-based inference for the University of Nebraska-Lincoln. The course design was informed by the Community of Inquiry framework (Garrison, Anderson, …

Fully Bayesian Analysis Of Relevance Vector Machine Classification With Probit Link Function For Imbalanced Data Problem, Wenyang Wang, Dongchu Sun, Peng Shao, Haibo Kuang, Cong Sui

Department of Statistics: Faculty Publications

The original RVM classification model uses the logistic link function to build the likelihood function making the model hard to be conducted since the posterior of the weight parameter has no closed-form solution. This article proposes the probit link function approach instead of the logistic one for the likelihood function in the RVM classification model, namely PRVM (RVM with the probit link function). We show that the posterior of the weight parameter in PRVM follows the Multivariate Normal distribution and achieves a closed-form solution. A latent variable is needed in our algorithms to simplify the Bayesian computation greatly, and its …

Forecasting Of The Covid-19 Epidemic: A Scientometric Analysis, Pandri Ferdias, Ansari Saleh Ahmar

Library Philosophy and Practice (e-journal)

This study presented a scientometric analysis of scientific publications with discussions of forecasting and COVID-19. The data of this study were obtained from the Scopus database using the keywords: ( TITLE-ABS-KEY (forecast) AND TITLE-ABS-KEY (covid)) and the data were taken on March 26, 2021. This study was a scientometric study. The data were subsequently analyzed using the VosViewer and Bibliometrix R Package. The results showed that “COVID-19” was the keyword most frequently used by researchers, followed by “forecasting” and “human”. Authors who discussed the topic of forecasting COVID-19 come from 83 different countries/regions, with the most articles sent by authors …

Analysis And Publication Profile Of Indonesian Scientific Work In 2020 Based On The Scopus Database, Akbar Iskandar, Nico Djundharto Djajasinga, Andi Dirga Noegraha, Erwin Gatot, Ansari Saleh Ahmar

Library Philosophy and Practice (e-journal)

This research was conducted to identify and describe the profile of publications in Indonesia in 2020. This research used the bibliometric methods. The data in this research were collected by searching through the Scopus database with the keywords: AFFILCOUNTRY “Indonesia” and PUBYEAR “2020” with the exception of AFFILCOUNTRY other than “Indonesia”. Data were then analyzed based on author affiliation, subject, document type, source type, source title, and language. The results of the research indicated that the development of Indonesian scientific publications was dominated by article types (50.69%) and conference papers (45.83%) with the subject area of publication dominated by engineering, …

Development Of A Multiplex Real-Time Pcr Assay For Predicting Macrolide And Tetracycline Resistance Associated With Bacterial Pathogens Of Bovine Respiratory Disease, Enakshy Dutta, John Loy, Caitlyn A. Deal, Emily L. Wynn, Michael L. Clawson, Jennifer Clarke, Bing Wang

Department of Statistics: Faculty Publications

Antimicrobial resistance (AMR) in bovine respiratory disease (BRD) is an emerging concern that may threaten both animal and public health. Rapid and accurate detection of AMR is essential for prudent drug therapy selection during BRD outbreaks. This study aimed to develop a multiplex quantitative real-time polymerase chain reaction assay (qPCR) to provide culture-independent information regarding the phenotypic AMR status of BRD cases and an alternative to the gold-standard, culture-dependent test. Bovine clinical samples (297 lung and 111 nasal) collected in Nebraska were subjected to qPCR quantification of macrolide (MAC) and tetracycline (TET) resistance genes and gold-standard determinations of AMR of …

A Review Of Spatial Causal Inference Methods For Environmental And Epidemiological Applications, Brian J. Reich, Shu Yang, Yawen Guan, Andrew B. Giffin, Matthew J. Miller, Ana Rappold

Department of Statistics: Faculty Publications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines but have only recently begun to take hold in spatial applications. Spatial causal inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to …

Treatment Of Inconclusive Results In Firearms Error Rate Studies, Heike Hofmann, Susan Vanderplas, Alicia L. Carriquiry

Department of Statistics: Faculty Publications

★ Defining error rates for firearms evidence ★ Impact of inconclusive decisions on error rates ★ Predictive probabilities and errors

Incorporating Animal Movement Into Distance Sampling, R. Glennie, S. T. Buckland, R. Langrock, Tim Gerrodette, Susan Chivers, M. D. Scott

United States Department of Commerce: Staff Publications

Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with animal telemetry data. Detection probability depends on the entire unobserved path the animal …

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 …