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Articles 1 - 30 of 81
Full-Text Articles in Statistical Models
Seasonal Resource Selection And Habitat Treatment Use By A Fringe Population Of Greater Sage-Grouse, Rhett Boswell
Seasonal Resource Selection And Habitat Treatment Use By A Fringe Population Of Greater Sage-Grouse, Rhett Boswell
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
Movement and habitat selection by Greater Sage-grouse (Centrocercus uropasianus) is of great interest to wildlife managers tasked with applying conservation measures for this iconic western species. Current technology has created small and lightweight GPS (Global Positioning Systems) transmitters that can be attached to sage-grouse. Using GIS software and statistical programs such as Program R, land managers can analyze GPS location data to assess how sage-grouse are geospatially interacting with their habitats. Within the Panguitch Sage-Grouse Management Area (SGMA) thousands of acres of land have been restored or manipulated to enhance sage-grouse habitat; this usually involves removal of pinyon pine …
Some Dimension Reduction Strategies For The Analysis Of Survey Data, Jiaying Weng, Derek S. Young
Some Dimension Reduction Strategies For The Analysis Of Survey Data, Jiaying Weng, Derek S. Young
Statistics Faculty Publications
In the era of big data, researchers interested in developing statistical models are challenged with how to achieve parsimony. Usually, some sort of dimension reduction strategy is employed. Classic strategies are often in the form of traditional inference procedures, such as hypothesis testing; however, the increase in computing capabilities has led to the development of more sophisticated methods. In particular, sufficient dimension reduction has emerged as an area of broad and current interest. While these types of dimension reduction strategies have been employed for numerous data problems, they are scantly discussed in the context of analyzing survey data. This …
Statistical Analysis Of Momentum In Basketball, Mackenzi Stump
Statistical Analysis Of Momentum In Basketball, Mackenzi Stump
Honors Projects
The “hot hand” in sports has been debated for as long as sports have been around. The debate involves whether streaks and slumps in sports are true phenomena or just simply perceptions in the mind of the human viewer. This statistical analysis of momentum in basketball analyzes the distribution of time between scoring events for the BGSU Women’s Basketball team from 2011-2017. We discuss how the distribution of time between scoring events changes with normal game factors such as location of the game, game outcome, and several other factors. If scoring events during a game were always randomly distributed, or …
Developing Leading And Lagging Indicators To Enhance Equipment Reliability In A Lean System, Dhanush Agara Mallesh
Developing Leading And Lagging Indicators To Enhance Equipment Reliability In A Lean System, Dhanush Agara Mallesh
Masters Theses
With increasing complexity in equipment, the failure rates are becoming a critical metric due to the unplanned maintenance in a production environment. Unplanned maintenance in manufacturing process is created issues with downtimes and decreasing the reliability of equipment. Failures in equipment have resulted in the loss of revenue to organizations encouraging maintenance practitioners to analyze ways to change unplanned to planned maintenance. Efficient failure prediction models are being developed to learn about the failures in advance. With this information, failures predicted can reduce the downtimes in the system and improve the throughput.
The goal of this thesis is to predict …
Bayesian Model For Detection Of Outliers In Linear Regression With Application To Longitudinal Data, Zahraa Al-Sharea
Bayesian Model For Detection Of Outliers In Linear Regression With Application To Longitudinal Data, Zahraa Al-Sharea
Graduate Theses and Dissertations
Outlier detection is one of the most important challenges with many present-day applications. Outliers can occur due to uncertainty in data generating mechanisms or due to an error in data recording/processing. Outliers can drastically change the study's results and make predictions less reliable. Detecting outliers in longitudinal studies is quite challenging because this kind of study is working with observations that change over time. Therefore, the same subject can produce an outlier at one point in time produce regular observations at all other time points. A Bayesian hierarchical modeling assigns parameters that can quantify whether each observation is an outlier …
Novel Statistical Models For Quantitative Shape-Gene Association Selection, Xiaotian Dai
Novel Statistical Models For Quantitative Shape-Gene Association Selection, Xiaotian Dai
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
Other research reported that genetic mechanism plays a major role in the development process of biological shapes. The primary goal of this dissertation is to develop novel statistical models to investigate the quantitative relationships between biological shapes and genetic variants. However, these problems can be extremely challenging to traditional statistical models for a number of reasons: 1) the biological phenotypes cannot be effectively represented by single-valued traits, while traditional regression only handles one dependent variable; 2) in real-life genetic data, the number of candidate genes to be investigated is extremely large, and the signal-to-noise ratio of candidate genes is expected …
Making Models With Bayes, Pilar Olid
Making Models With Bayes, Pilar Olid
Electronic Theses, Projects, and Dissertations
Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects …
Variational Bayes Estimation Of Discrete-Margined Copula Models With Application To Ime Series, Ruben Loaiza-Maya, Michael S. Smith
Variational Bayes Estimation Of Discrete-Margined Copula Models With Application To Ime Series, Ruben Loaiza-Maya, Michael S. Smith
Michael Stanley Smith
Statistical Modelling, Optimal Strategies And Decisions In Two-Period Economies, Jiang Wu
Statistical Modelling, Optimal Strategies And Decisions In Two-Period Economies, Jiang Wu
Electronic Thesis and Dissertation Repository
Motivated by some real problems, our thesis puts forward two general two-period pricing models and explore optimal buying and selling strategies in two states of the two-period decision, when buyer/seller's decisions in the two periods are uncertain: commodity valuations may or may not be independent, may or may not follow the same distribution, be heavily or just lightly influenced by exogenous economic conditions, and so on. For both the example of buying laptops and the example of selling houses, the connections between each example and the two-envelope paradox encourage us to explore optimal strategies based on the works of McDonnell …
Data-Adaptive Kernel Support Vector Machine, Xin Liu
Data-Adaptive Kernel Support Vector Machine, Xin Liu
Electronic Thesis and Dissertation Repository
In this thesis, we propose the data-adaptive kernel Support Vector Machine (SVM), a new method with a data-driven scaling kernel function based on real data sets. This two-stage approach of kernel function scaling can enhance the accuracy of a support vector machine, especially when the data are imbalanced. Followed by the standard SVM procedure in the first stage, the proposed method locally adapts the kernel function to data locations based on the skewness of the class outcomes. In the second stage, the decision rule is constructed with the data-adaptive kernel function and is used as the classifier. This process enlarges …
Juvenile River Herring In Freshwater Lakes: Sampling Approaches For Evaluating Growth And Survival, Matthew T. Devine
Juvenile River Herring In Freshwater Lakes: Sampling Approaches For Evaluating Growth And Survival, Matthew T. Devine
Masters Theses
River herring, collectively alewives (Alosa pseudoharengus) and blueback herring (A. aestivalis), have experienced substantial population declines over the past five decades due in large part to overfishing, combined with other sources of mortality, and disrupted access to critical freshwater spawning habitats. Anadromous river herring populations are currently assessed by counting adults in rivers during upstream spawning migrations, but no field-based assessment methods exist for estimating juvenile densities in freshwater nursery habitats. Counts of 4-year-old migrating adults are variable and prevent understanding about how mortality acts on different life stages prior to returning to spawn (e.g., juveniles …
Modelling Bird Migration With Motus Data And Bayesian State-Space Models, Justin Baldwin
Modelling Bird Migration With Motus Data And Bayesian State-Space Models, Justin Baldwin
Masters Theses
Bird migration is a poorly-known yet important phenomenon, as understanding movement patterns of birds can inform conservation strategies and public health policy for animal-borne diseases. Recent advances in wildlife tracking technology, in particular the Motus system, have allowed researchers to track even small flying birds and insects with radio transmitters that weigh fractions of a gram. This system relies on a community-based distributed sensor network that detects tagged animals as they move through the detection nodes on journeys that range from small local movements to intercontinental migrations. The quantity of data generated by the Motus system is unprecedented, is on …
Using Multivariate Statistical Techniques To Aid In A Sports Index Construction, Tiffany Kelly
Using Multivariate Statistical Techniques To Aid In A Sports Index Construction, Tiffany Kelly
Mathematics Colloquium Series
Within a quantitative career, you are/will soon be challenged to create an overall value to explain a situational status. For example, socio-economic status, well-being, and in this specific example, happiness among sports fans. This talk seeks to discuss my previous work developed out from student research performed at NSU in its application to my first project for ESPN Sports Analytics, the College Football Fan Happiness Index (http://es.pn/2vmParA) . I will dive into the multivariate statistical techniques of principal component analysis and hierarchal clustering to create this happiness index from a slew of variables.
Latent Storm Factors And Their Indicators, Joy D'Andrea
Latent Storm Factors And Their Indicators, Joy D'Andrea
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Handguns And Hotspots: Spatio- Temporal Models For Gun Violence In Chicago,Il, Shelby Scott
Handguns And Hotspots: Spatio- Temporal Models For Gun Violence In Chicago,Il, Shelby Scott
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, Daniel Prawira
On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, Daniel Prawira
Electronic Thesis and Dissertation Repository
In family studies, we are interested in estimating the penetrance function of the event of interest in the presence of competing risks. Failure to account for competing risks may lead to bias in the estimation of the penetrance function. In this thesis, three statistical challenges are addressed: clustering, missing data, and competing risks. We proposed the cause-specific model with shared frailty and ascertainment correction to account for clustering and competing risks along with ascertainment of families into study. Multiple imputation is used to account for missing data. The simulation study showed good performance of our proposed model in estimating the …
A Geometric Derivation Of The Irwin-Hall Distribution, James E. Marengo, Lucas Stefanic, David L. Farnsworth
A Geometric Derivation Of The Irwin-Hall Distribution, James E. Marengo, Lucas Stefanic, David L. Farnsworth
Articles
The Irwin-Hall distribution is the distribution of the sum of a finite number of independent identically distributed uniform random variables on the unit interval. Many applications arise since round-off errors have a transformed Irwin-Hall distribution and the distribution supplies spline approximations to normal distributions. We review some of the distribution’s history. The present derivation is very transparent, since it is geometric and explicitly uses the inclusion-exclusion principle. In certain special cases, the derivation can be extended to linear combinations of independent uniform random variables on other intervals of finite length.The derivation adds to the literature about methodologies for finding distributions …
Models And Policies Of Port Carbon Emission Reduction: A Case Study Of The Port Of Dalian, Jiaqiong Zhao
Models And Policies Of Port Carbon Emission Reduction: A Case Study Of The Port Of Dalian, Jiaqiong Zhao
World Maritime University Dissertations
No abstract provided.
Annuity Product Valuation And Risk Measurement Under Correlated Financial And Longevity Risks, Soohong Park
Annuity Product Valuation And Risk Measurement Under Correlated Financial And Longevity Risks, Soohong Park
Electronic Thesis and Dissertation Repository
Longevity risk is a non-diversifiable risk and regarded as a pressing socio-economic challenge of the century. Its accurate assessment and quantification is therefore critical to enable pension-fund companies provide sustainable old-age security and maintain a resilient global insurance market. Fluctuations and a decreasing trend in mortality rates, which give rise to longevity risk, as well as the uncertainty in interest-rate dynamics constitute the two fundamental determinants in pricing and risk management of longevity-dependent products. We also note that historical data reveal some evidence of strong correlation between mortality and interest rates and must be taken into account when modelling their …
Examination And Comparison Of The Performance Of Common Non-Parametric And Robust Regression Models, Gregory F. Malek
Examination And Comparison Of The Performance Of Common Non-Parametric And Robust Regression Models, Gregory F. Malek
Electronic Theses and Dissertations
ABSTRACT
Examination and Comparison of the Performance of Common Non-Parametric and Robust Regression Models
By
Gregory Frank Malek
Stephen F. Austin State University, Masters in Statistics Program,
Nacogdoches, Texas, U.S.A.
This work investigated common alternatives to the least-squares regression method in the presence of non-normally distributed errors. An initial literature review identified a variety of alternative methods, including Theil Regression, Wilcoxon Regression, Iteratively Re-Weighted Least Squares, Bounded-Influence Regression, and Bootstrapping methods. These methods were evaluated using a simple simulated example data set, as well as various real data sets, including math proficiency data, Belgian telephone call data, and faculty …
Camel And Adcirc Storm Surge Models—A Comparative Study, Muhammad K. Akbar, Richard A. Luettich, Jason G. Fleming, Shahrouz K. Aliabadi
Camel And Adcirc Storm Surge Models—A Comparative Study, Muhammad K. Akbar, Richard A. Luettich, Jason G. Fleming, Shahrouz K. Aliabadi
Mechanical and Manufacturing Engineering Faculty Research
The Computation and Modeling Engineering Laboratory (CaMEL), an implicit solver-based storm surge model, has been extended for use on high performance computing platforms. An MPI (Message Passing Interface) based parallel version of CaMEL has been developed from the previously existing serial version. CaMEL uses hybrid finite element and finite volume techniques to solve shallow water conservation equations in either a Cartesian or a spherical coordinate system and includes hurricane-induced wind stress and pressure, bottom friction, the Coriolis effect, and tidal forcing. Both semi-implicit and fully-implicit time stepping formulations are available. Once the parallel implementation is properly validated, CaMEL is evaluated …
Thermodynamics Of Coherent Structures Near Phase Transitions, Julia M. Meyer, Ivan Christov
Thermodynamics Of Coherent Structures Near Phase Transitions, Julia M. Meyer, Ivan Christov
The Summer Undergraduate Research Fellowship (SURF) Symposium
Phase transitions within large-scale systems may be modeled by nonlinear stochastic partial differential equations in which system dynamics are captured by appropriate potentials. Coherent structures in these systems evolve randomly through time; thus, statistical behavior of these fields is of greater interest than particular system realizations. The ability to simulate and predict phase transition behavior has many applications, from material behaviors (e.g., crystallographic phase transformations and coherent movement of granular materials) to traffic congestion. Past research focused on deriving solutions to the system probability density function (PDF), which is the ground-state wave function squared. Until recently, the extent to which …
Using Mountain Snowpack To Predict Summer Water Availability In Semiarid Mountain Watersheds, Rebecca Dawn Garst
Using Mountain Snowpack To Predict Summer Water Availability In Semiarid Mountain Watersheds, Rebecca Dawn Garst
Boise State University Theses and Dissertations
In the mountainous landscapes of the western United States, water resources are dominated by snowpack. As temperatures rise in spring and summer, the melting snow produces an increase in river flow levels. Reservoirs are used during this increase to retain surplus water, which is released to supplement growing season water supply once the peak flows decrease to below water demands. Once there is no longer surplus natural flow of water, the water accounting changes – referred to as the day of allocation (DOA), and water previously retained within the reservoir is used to supplement the lower flow levels. The amount …
Data Analysis Methods Using Persistence Diagrams, Andrew Marchese
Data Analysis Methods Using Persistence Diagrams, Andrew Marchese
Doctoral Dissertations
In recent years, persistent homology techniques have been used to study data and dynamical systems. Using these techniques, information about the shape and geometry of the data and systems leads to important information regarding the periodicity, bistability, and chaos of the underlying systems. In this thesis, we study all aspects of the application of persistent homology to data analysis. In particular, we introduce a new distance on the space of persistence diagrams, and show that it is useful in detecting changes in geometry and topology, which is essential for the supervised learning problem. Moreover, we introduce a clustering framework directly …
Supervised Classification Using Finite Mixture Copula, Sumen Sen, Norou Diawara
Supervised Classification Using Finite Mixture Copula, Sumen Sen, Norou Diawara
Mathematics & Statistics Faculty Publications
Use of copula for statistical classification is recent and gaining popularity. For example, statistical classification using copula has been proposed for automatic character recognition, medical diagnostic and most recently in data mining. Classical discrimination rules assume normality. But in this data age time, this assumption is often questionable. In fact features of data could be a mixture of discrete and continues random variables. In this paper, mixture copula densities are used to model class conditional distributions. Such types of densities are useful when the marginal densities of the vector of features are not normally distributed and are of a mixed …
A Characterization Of A Value Added Model And A New Multi-Stage Model For Estimating Teacher Effects Within Small School Systems, Julie M. Garai
A Characterization Of A Value Added Model And A New Multi-Stage Model For Estimating Teacher Effects Within Small School Systems, Julie M. Garai
Department of Statistics: Dissertations, Theses, and Student Work
At both the national and state level there is increasing pressure to develop metrics to determine if school systems are meeting educational objectives. All states mandate some form of assessment by standardized tests. One method currently used to model student test scores is Value Added Modeling (VAM), which models student scores as a product of classroom and school environments. One VAM approach is the Tennessee Value Added Assessment System (TVAAS) which models student gains from year to year. Teacher effects are included in this layered model, which estimates the teacher’s added value to a student score through best linear unbiased …
Bayesian Model Averaging With Change Points To Assess The Impact Of Vaccination And Public Health Interventions., Esra Kürüm, Joshua L Warren, Cynthia Schuck-Paim, Roger Lustig, Joseph A Lewnard, Rodrigo Fuentes, Christian A W Bruhn, Robert J Taylor, Lone Simonsen, Daniel M Weinberger
Bayesian Model Averaging With Change Points To Assess The Impact Of Vaccination And Public Health Interventions., Esra Kürüm, Joshua L Warren, Cynthia Schuck-Paim, Roger Lustig, Joseph A Lewnard, Rodrigo Fuentes, Christian A W Bruhn, Robert J Taylor, Lone Simonsen, Daniel M Weinberger
Global Health Faculty Publications
Background: Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates.
Methods: We assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other …
Application Of Support Vector Machine Modeling And Graph Theory Metrics For Disease Classification, Jessica M. Rudd
Application Of Support Vector Machine Modeling And Graph Theory Metrics For Disease Classification, Jessica M. Rudd
Published and Grey Literature from PhD Candidates
Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with …
Classification With Large Sparse Datasets: Convergence Analysis And Scalable Algorithms, Xiang Li
Classification With Large Sparse Datasets: Convergence Analysis And Scalable Algorithms, Xiang Li
Electronic Thesis and Dissertation Repository
Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, etc. Linear classifiers are popular choices for classifying such datasets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered.
1. Sparse data and convergence behavior. How different properties of a dataset, such as …
Testing The Independence Hypothesis Of Accepted Mutations For Pairs Of Adjacent Amino Acids In Protein Sequences, Jyotsna Ramanan, Peter Revesz
Testing The Independence Hypothesis Of Accepted Mutations For Pairs Of Adjacent Amino Acids In Protein Sequences, Jyotsna Ramanan, Peter Revesz
School of Computing: Faculty Publications
Evolutionary studies usually assume that the genetic mutations are independent of each other. However, that does not imply that the observed mutations are independent of each other because it is possible that when a nucleotide is mutated, then it may be biologically beneficial if an adjacent nucleotide mutates too. With a number of decoded genes currently available in various genome libraries and online databases, it is now possible to have a large-scale computer-based study to test whether the independence assumption holds for pairs of adjacent amino acids. Hence the independence question also arises for pairs of adjacent amino acids within …