Statistics and Probability Commons^™

Flexible Box-Cox Transformation Model For Analyzing Energy Usage At Uconn, Yutong Chen

Honors Scholar Theses

The Box-Cox transformation is a way to transform non-normal data into more normally distributed data. However, when we fit linear regression models to transformed data, we cannot use the Akaike Information Criterion (AIC) directly to compare different models since the transformed data are no longer on the same scale. In this study, the Jacobian adjusted AIC is proposed to compare regression models on transformed data and to select an “optimal” value of the transformation parameter. Instead of using a single for the whole data, which is commonly used in the literature and in practice, we formulate a linear regression pattern …

Bayesian Variable Selection And Post-Selection Inference, Qiyiwen Zhang

Arts & Sciences Graduate Student Theses and Dissertations

In this dissertation, we first develop a novel perspective to compare Bayesian variable selection procedures in terms of their selection criteria as well as their finite-sample properties. Secondly, we investigate Bayesian post-selection inference in two types of selection problems: linear regression and population selection. We will demonstrate that both inference problems are susceptible to selection effects since the selection procedure is data-dependent. Before comparing Bayesian variable selection procedures, we first classify the current Bayesian variable selection procedures into two classes: those with selection criteria defined on the space of candidate models, and those with selection criteria not explicitly formulated on …

Multilevel Asymptotic Parallel-In-Time Techniques For Temporally Oscillatory Pdes, Nicholas Abel

Mathematics & Statistics ETDs

As the clock speeds of individual processors level off and the amount of parallel resources continue to increase rapidly, further exploitation of parallelism is necessary to improve compute times. For time-dependent differential equations, the serial computation of time-stepping presents a bottleneck, but parallel-in-time integration methods offer a way to compute the solution in parallel along the time domain. Parallel-in-time methods have been successful in achieving speedup when computing solutions for parabolic problems; however, for problems with large hyperbolic terms and no strong diffusivity, parallel-in-time methods have traditionally struggled to offer speedup. While work has been done to understand why parallel-in-time …

“The Prediction Of Fantasy Football”, Chelsea Robinson

Mathematics Senior Capstone Papers

In this paper, we consider the game fantasy football, which allows people to simulate being a National Football League team owner. Imaginary owners select from the best players in the NFL and compete on weekly basis based upon player performances on the field. Fantasy football has become popular over the years. In 2011, according to the Fantasy Sports Trade Association there were 35 million people that played fantasy sports online in the United States and Canada. The most major companies that use fantasy football are Yahoo, ESPN, and NFL, even though there are more platforms. Many people use these platforms …

The Primary Volatile Composition Of Comet C/2015 Er61 (Panstarrs), Aaron Butler

Theses

In the outer edges of the solar system exist two regions: the Kuiper belt and Oort cloud. These two regions have a high amount of icy bodies (comets) orbiting the Sun. Comets located within the Oort cloud and Kuiper belt contain an ancient codex to the solar systems contents, before the formation of our solar system. Presented are near-infrared, high-resolution (λ/Δλ ~40000) data obtained from the immersion-grating echelle spectrograph iSHELL at the 3m NASA Infrared Telescope Facility (IRTF) in Maunakea, Hawaii of the Oort cloud comet C/2015 ER61 (PANSTARRS). Observations took place on April 15 and 17 in 2017 while …

Mentoring Undergraduate Research In Statistics: Reaping The Benefits And Overcoming The Barriers, Joseph R. Nolan, Kelly S. Mcconville, Vittorio Addona, Nathan L. Tintle, Dennis K. Pearl

Faculty Work Comprehensive List

Undergraduate research experiences (UREs), whether within the context of a mentor-mentee experience or a classroom framework, represent an excellent opportunity to expose students to the independent scholarship model. The high impact of undergraduate research has received recent attention in the context of STEM disciplines. Reflecting a 2017 survey of statistics faculty, this article examines the perceived benefits of UREs, as well as barriers to the incorporation of UREs, specifically within the field of statistics. Viewpoints of students, faculty mentors, and institutions are investigated. Further, the article offers several strategies for leveraging characteristics unique to the field of statistics to overcome …

Forecasting Daily Stock Market Return With Multiple Linear Regression, Shengxuan Chen

Mathematics Senior Capstone Papers

The purpose of this project is to use data mining and big data analytic techniques to forecast daily stock market return with multiple linear regression. Using mathematical and statistical models to analyze the stock market is important and challenging. The accuracy of the final results relies on the quality of the input data and the validity of the methodology. In the report, within 5-year period, the data regarding eleven financial and economical features are observed and recorded on each trading day. After preprocessing the raw data with statistical method, we use the multiple linear regression to predict the daily return …

Predictive Modeling Of Asynchronous Event Sequence Data, Jin Shang

LSU Doctoral Dissertations

Large volumes of temporal event data, such as online check-ins and electronic records of hospital admissions, are becoming increasingly available in a wide variety of applications including healthcare analytics, smart cities, and social network analysis. Those temporal events are often asynchronous, interdependent, and exhibiting self-exciting properties. For example, in the patient's diagnosis events, the elevated risk exists for a patient that has been recently at risk. Machine learning that leverages event sequence data can improve the prediction accuracy of future events and provide valuable services. For example, in e-commerce and network traffic diagnosis, the analysis of user activities can be …

Decision Tree For Predicting The Party Of Legislators, Afsana Mimi

Publications and Research

The motivation of the project is to identify the legislators who voted frequently against their party in terms of their roll call votes using Office of Clerk U.S. House of Representatives Data Sets collected in 2018 and 2019. We construct a model to predict the parties of legislators based on their votes. The method we used is Decision Tree from Data Mining. Python was used to collect raw data from internet, SAS was used to clean data, and all other calculations and graphical presentations are performed using the R software.

A Statistical Analysis Of The Unm Facets Design Identity & Beliefs Survey Data, Clarissa A. Sorensen-Unruh

Mathematics & Statistics ETDs

The NSF-funded FACETS (Formation of Accomplished Chemical Engineers for Transforming Society, NSF Award 1623105) grant aims to transform the undergraduate engineering experience in the Department of Chemical and Biological Engineering at the University of New Mexico to address attrition within engineering majors, especially among underserved populations (Brainard & Carlin, 1998). The UNM FACETS Design Identity & Beliefs survey, an assessment tool used as part of the research of the grant, generated the dataset used in this study. I performed several different statistical analyses on the dataset, including confirmatory factor analysis (CFA), principal component analysis (PCA), and cluster analysis. The …

Statistical Analysis Of Land Cover Conversion Trends In Northwest Ohio, Chaska Mcgowan

Honors Projects

Agricultural land in the U.S. is abundant but not infinite. Change in cropland impacts national and local economies and the natural environment. The Black Swamp Conservancy (BSC), a non-profit land trust in Perrysburg, Ohio, is committed to preserving agricultural and natural lands in the Northwest Ohio region for future generations. This project was designed in collaboration with the BSC to illustrate the spatial distribution of land cover change within their sixteen-county service area in Northwest Ohio and to find a list of factors associated with land cover change in the region. The primary data source was the National Land Cover …

Can Auxiliary Information Improve Rasch Estimation At Small Sample Sizes?, Derek Sauder

Dissertations, 2020-current

The Rasch model is commonly used to calibrate multiple choice items. However, the sample sizes needed to estimate the Rasch model can be difficult to attain (e.g., consider a small testing company trying to pretest new items). With small sample sizes, auxiliary information besides the item responses may improve estimation of the item parameters. The purpose of this study was to determine if incorporating item property information (i.e., characteristics of the items related to item difficulty) in a random effects linear logistic test model (RE-LLTM) would improve estimation of item difficulty. A simulation study was conducted that varied sample size, …

Modeling Species Distribution And Habitat Suitability Of American Ginseng (Panax Quinquefolius) In Virginia, Jacob D. J. Peters

Masters Theses, 2020-current

American ginseng (Panax quinquefolius) is a well-known and sought-after medicinal plant native to North America that is facing increased threat of extinction due to overharvesting, herbivory, and habitat loss. Species distribution and habitat suitability models may be valuable to landowners interested in sustainable harvest or to institutions interested in the conservation and restoration of the species. With unequal sampling efforts across a region of interest, it is likely that some locations with appropriate habitat may be misrepresented in model predictions. This study refined a state-derived species distribution model for ginseng through increased sampling effort across the Cumberland Plateau …

Splitting Up A Complex Mess: The Effectiveness Of Statistical Analysis On Delimiting Species Complexes, Sara N. Schoen

Masters Theses, 2020-current

Recent studies have highlighted a need for more refined tools in species delimitation. This is especially true when considering diversity within species complexes, where members are morphologically similar and traditional tools have thus far failed to provide clearly defined boundaries between species. This project seeks to refine our traditional tools of species delimitation and apply new tools to the challenges created by species complexes. The focus organisms of this study are the anurans of the Limnonectes kuhlii complex. This species complex comprises more than 25 species of stream frogs from Southeast Asia. Traditionally, morphometrics (particularly linear measures) has been the …

Propensity Score Matching And Generalized Boosted Modeling In The Context Of Model Misspecification: A Simulation Study, Briana G. Craig

Masters Theses, 2020-current

In the absence of random assignment, researchers must consider the impact of selection bias – pre-existing covariate differences between groups due to differences among those entering into treatment and those otherwise unable to participate. Propensity score matching (PSM) and generalized boosted modeling (GBM) are two quasi-experimental pre-processing methods that strive to reduce the impact of selection bias before analyzing a treatment effect. PSM and GBM both examine a treatment and comparison group and either match or weight members of those groups to create new, balanced groups. The new, balanced groups theoretically can then be used as a proxy for the …

Attack And Defense In Security Analytics, Yiyun Zhou

Doctor of Data Science and Analytics Dissertations

The security problem has gained increasing awareness due to the various kinds of global threats. Security analytics is the process of using streaming data acquisition, collection, and artificial intelligence algorithms for security monitoring and threat disclosure. In this dissertation work, we utilize practical data-driven security analytics to identify the potential threat and explore the robustness of the machine learning model. We focus on two aspects: (1) Security Analytics: utilize machine learning and statistical analytics tools to identify and resolve the threat in real life, such as cybersecurity, abnormal activities. (2) Analytic Security: Explore the security issues of the machine learning …

Age At Migration And The Risk Of Psychotic Disorders: A Systematic Review And Meta-Analysis., Kelly K. Anderson, Jordan Edwards

Epidemiology and Biostatistics Publications

OBJECTIVE: To conduct a systematic review and meta-analysis of the existing evidence on the association between age at migration and the risk of psychotic disorders.

METHODS: Observational studies were eligible for inclusion if they presented data on the association between age at migration and the risk of psychotic disorders among first-generation migrant groups. We used two random effects meta-analyses to pool effect estimates for each stratum of age at migration relative to (i) a native-born reference category and (ii) the youngest age stratum (0 to 2 years).

RESULTS: Ten studies met inclusion criteria, and five were included in the meta-analysis. …

The Two Types Of Society: Computationally Revealing Recurrent Social Formations And Their Evolutionary Trajectories, Lux Miranda

Undergraduate Honors Capstone Projects

Comparative social science has a long history of attempts to classify societies and cultures in terms of shared characteristics. However, only recently has it become feasible to conduct quantitative analysis of large historical datasets to mathematically approach the study of social complexity and classify shared societal characteristics. Such methods have the potential to identify recurrent social formations in human societies and contribute to social evolutionary theory. However, in order to achieve this potential, repeated studies are needed to assess the robustness of results to changing methods and data sets. Using an improved derivative of the Seshat: Global History Databank, we …

Simplicity As A New Environmental Virtue, Justin Wheeler

Undergraduate Honors Capstone Projects

This paper argues for the addition of a new environmentally focused virtue, simplicity, to the virtue ethical framework developed by Aristotle. First, relevant background from Aristotle’s virtue ethics are developed including the crucial, “doctrine of the mean”, a balance between excess and deficiency of a specified character trait. The tenets of the new virtue simplicity are developed with practical examples based on Aristotle’s method of developing a virtue of character. Simplicity is proposed as a desire to take the appropriate amount from the natural world and an acceptance of one’s circumstances. Those possessing simplicity will not fall victim to the …

Demystification Of Graph And Information Entropy, Bryce Frederickson

Undergraduate Honors Capstone Projects

Shannon entropy is an information-theoretic measure of unpredictability in probabilistic models. Recently, it has been used to form a tool, called the von Neumann entropy, to study quantum mechanics and network flows by appealing to algebraic properties of graph matrices. But still, little is known about what the von Neumann entropy says about the combinatorial structure of the graphs themselves. This paper gives a new formulation of the von Neumann entropy that describes it as a rate at which random movement settles down in a graph. At the same time, this new perspective gives rise to a generalization of von …