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Articles 1 - 8 of 8
Full-Text Articles in Categorical Data Analysis
Online Prediction Of Streaming Data, Aleena Chanda
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 …
Variable Selection In Distance Metric Learning And Triplet Constraints For Deep Learning Based Ordinal Classification, James D. Clothier
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 …
Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson
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 …
Detection Of Deficiencies And Data Analysis Of Bridge Members With Deep Convolutional Neural Networks, Bennett Jackson
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 …
Split Classification Model For Complex Clustered Data, Katherine Gerot
Split Classification Model For Complex Clustered Data, Katherine Gerot
Honors Program: Senior Projects (Public)
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.
Role Of Misclassification Estimates In Estimating Disease Prevalence And A Non-Linear Approach To Study Synchrony Using Heart Rate Variability In Chickens, Dola Pathak
Department of Statistics: Dissertations, Theses, and Student Research
Infectious disease assays can be imperfect. When estimating disease prevalence, these imperfections are accounted for by incorporating assay sensitivity and specificity into point and variance estimates. Unfortunately, these accuracy measures are often treated as fixed constants, rather than acknowledging that they are estimates from an assay validation process. The purpose of this study is to show the detrimental effect of not taking into account this sampling variability when samples are obtained through group testing (aka, pooled testing). We show that confidence interval coverage can dramatically decline as the sample size increases for the main sample of interest. As a remedy …
Group Testing Regression Models, Boan Zhang
Group Testing Regression Models, Boan Zhang
Department of Statistics: Dissertations, Theses, and Student Research
Group testing, where groups of individual specimens are composited to test for the presence or absence of a disease (or some other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Statistical research in group testing has traditionally focused on a homogeneous population, where individuals are assumed to have the same probability of having a disease. However, individuals often have different risks of positivity, so recent research has examined regression models that allow for heterogeneity among individuals within the population. This dissertation focuses on two problems involving group testing regression models. …
The Em Algorithm For Group Testing Regression Models Under Matrix Pooling, Christopher R. Bilder, Boan Zhang
The Em Algorithm For Group Testing Regression Models Under Matrix Pooling, Christopher R. Bilder, Boan Zhang
Department of Statistics: Faculty Publications
No abstract provided.