Statistics and Probability Commons^™

An Exploration Of Parameter Duality In Statistical Inference, Suzanne Thornton, M. Xie

Mathematics & Statistics Faculty Works

Well-known debates among statistical inferential paradigms emerge from conflicting views on the notion of probability. One dominant view understands probability as a representation of sampling variability; another prominent view understands probability as a measure of belief. The former generally describes model parameters as fixed values, in contrast to the latter. We propose that there are actually two versions of a parameter within both paradigms: a fixed unknown value that generated the data and a random version to describe the uncertainty in estimating the unknown value. An inferential approach based on CDs deciphers seemingly conflicting perspectives on parameters and probabilities.

The Chains That Bind: Gender, Disability, Race, And It Accommodations, Eleanor T. Loiacono, Shiya Cao

Statistical and Data Sciences: Faculty Books

This chapter explores intersectionality of gender, disability, and race relevant to Information Technology (IT) accommodations and employment. More specifically, we investigate individuals’ experiences and differences in receiving IT accommodations as an organizational diversity intervention that helps disabled employees integrate into the workplace. The goal of this chapter is to seek a better understanding of individual differences in the accommodation process and how to empower disabled women in the workplace. To do so, by applying the Individual Differences Theory of Gender and IT (IDTGIT), we focus on the experiences disabled men and women have with regard to IT accommodations as well …

System For Scanning And Monitoring Science And Technology Policy Texts Of Us And Eu, Dahai Yu, Aofei Chang, Bolin Hua, Hongguang Wang, Wenjiao Zheng

Journal of Scientific Information Research

[Purpose/significance]Science and technology policy plays a guiding role in the development of science and technology. Whether science and technology policies are efficient and reasonable has an important impact on the rapid development of science and technology. In order to help decision-makers grasp the latest international scientific and technological layout, planning and policy guidance more quickly, especially to track and analyze the scientific, and technological policies of major developed countries in Europe and the United States, grab analyze and mine the corresponding scientific and technological policy texts in real time, has of great significance in the current international environment.[Method/process]This research designed …

Odd Solutions To Systems Of Inequalities Coming From Regular Chain Groups, Daniel Slilaty

Mathematics and Statistics Faculty Publications

Hoffman’s theorem on feasible circulations and Ghouila-Houry’s theorem on feasible tensions are classical results of graph theory. Camion generalized these results to systems of inequalities over regular chain groups. An analogue of Camion’s result is proved in which solutions can be forced to be odd valued. The obtained result also generalizes the results of Pretzel and Youngs as well as Slilaty. It is also shown how Ghouila-Houry’s result can be used to give a new proof of the graph- coloring theorem of Minty and Vitaver.

Carnivore And Ungulate Occurrence In A Fire-Prone Region, Sara J. Moriarty-Graves

Cal Poly Humboldt theses and projects

Increasing fire size and severity in the western United States causes changes to ecosystems, species’ habitat use, and interspecific interactions. Wide-ranging carnivore and ungulate mammalian species and their interactions may be influenced by an increase in fire activity in northern California. Depending on the fire characteristics, ungulates may benefit from burned habitat due to an increase in forage availability, while carnivore species may be differentially impacted, but ultimately driven by bottom-up processes from a shift in prey availability. I used a three-step approach to estimate the single-species occupancy of four large mammal species: mountain lion (Puma concolor), coyote …

Eeg-Based Spanish Language Proficiency Classification: An Eeg Power Spectrum And Cross-Spectrum Analysis, Blaise Xavier O'Mara, Skyler Baumer

Honors Theses and Capstones

Second language proficiency may be predicted with electrophysiological techniques. In a machine learning application, this electrophysiological data may be used for language instructors and language students to assess their language learning. This study identifies how electroencephalogram (EEG) power spectrum and cross spectrum data of the brain cortex relates to Spanish second language (L2) proficiency of 20 Spanish language students of varying proficiency levels at the University of New Hampshire. The two metrics for assessing cortical power and processing were event-related desynchronization (ERD)—a measure of relative change in power—of the alpha (8-12 Hz) brain frequency band, and alpha and beta (13-30Hz) …

Occurrence Of Per- And Polyfluoroalkyl Substances (Pfas) In New Hampshire Biosolids, Katherine A. Wieck

Honors Theses and Capstones

Per- and polyfluoroalkyl substances (PFAS) are a group of over 4,000 compounds used in the manufacturing of products including aqueous film forming foams for firefighting, stain repellents, waterproofing agents, and nonstick cookware since their initial development in the 1940s. The long fluorinated carbon chain structure of PFAS causes chemical and thermal stability, and thus resistance to biodegradation. Biosolids produced at wastewater facilities for uses such as agricultural land-applied compost and fertilizer for lawns and athletic fields, as well as sludge disposed in landfills can cause contamination of groundwater and surface water. This poses a significant threat to human and environmental …

Wilcoxon-Mann-Whitney Effects For Clustered Data: Informative Cluster Size, Changrui Liu

Theses and Dissertations--Statistics

In recent research, there has been a growing interest in understanding the impact of informative cluster size (ICS) on statistical inference for clustered data. In the non-parametric context, the problem for testing equality of distribution functions has been the main consideration. We are aiming to develop inferential procedures for the Wilcoxon-Mann-Whitney effect, also known as the non-parametric relative effect, involving two or more groups. Computationally, results from both simulated and real-world data have shown promising results that our proposed tests effectively account for ICS and they particularly outperform other methods in the literature designed for ignorable cluster sizes. The applications …

An Assessment Of "Long-Thin" Airline Routes: Network Structure And Emissions Implications For Environmental Policy, Porter Burns

All Master's Theses

The purpose of this research was to define, map, and quantify the network and environmental implications of “long-thin” routes (LTRs) – a route structure that has been discussed in the aviation industry but not formally studied in literature. LTRs were defined through the use of global OAG scheduling data from 1998 to 2018 to identify trends in air traffic growth and network dynamics. Flights were separated into seven aircraft class sizes (e.g., 75–150 seats, 150–225 seats) to measure LTRs at multiple scales. Routes were considered “long” if the stage length was at or above the 75^th percentile in each …

Expectile Neural Networks For Genetic Data Analysis Of Complex Diseases, Jinghang Lin, Xiaoran Tong, Chenxi Li, Qing Lu

Biostatistics Faculty Publications

The genetic etiologies of common diseases are highly complex and heterogeneous. Classic methods, such as linear regression, have successfully identified numerous variants associated with complex diseases. Nonetheless, for most diseases, the identified variants only account for a small proportion of heritability. Challenges remain to discover additional variants contributing to complex diseases. Expectile regression is a generalization of linear regression and provides complete information on the conditional distribution of a phenotype of interest. While expectile regression has many nice properties, it has rarely been used in genetic research. In this paper, we develop an expectile neural network (ENN) method for genetic …

Cheiloscopy Patterns In Individuals With And Without Parafunctional Oral Habits: A Cross-Sectional Observation Pilot Study, Emily Regan, Brenda Bradshaw, Ann Bruhn, Walter Melvin, Sinjini Sikdar

Dental Hygiene Faculty Publications

Purpose

Lip prints are unique and have potential for use as a human identifier. The purpose of this study was to observe possible cheiloscopy differences of individuals with and without parafunctional oral habits such as smoking, vaping, playing a wind instrument or using an asthma inhaler.

Methods

This IRB approved blinded cross-sectional observation pilot study collected lip prints from 66 individuals, three of which were excluded. Participants cleansed their lips, then lipstick was applied to the vermillion zones of the upper and lower lips. Adhesive tape was applied to the lips and prints were transferred to white bond paper for …

Joint Probability Analysis Of Extreme Precipitation And Water Level For Chicago, Illinois, Anna Li Holey

Dissertations, Master's Theses and Master's Reports

A compound flooding event occurs when there is a combination of two or more extreme factors that happen simultaneously or in quick succession and can lead to flooding. In the Great Lakes region, it is common for a compound flooding event to occur with a high lake water level and heavy rainfall. With the potential of increasing water levels and an increase in precipitation under climate change, the Great Lakes coastal regions could be at risk for more frequent and severe flooding. The City of Chicago which is located on Lake Michigan has a high population and dense infrastructure and …

Evaluation Of Edison's Data Science Competency Framework Through A Comparative Literature Analysis, Karl R. B. Schmitt, Linda Clark, Katherine M. Kinnaird, Ruth E. H. Wertz, Björn Sandstede

Statistical and Data Sciences: Faculty Publications

During the emergence of Data Science as a distinct discipline, discussions of what exactly constitutes Data Science have been a source of contention, with no clear resolution. These disagreements have been exacerbated by the lack of a clear single disciplinary 'parent.' Many early efforts at defining curricula and courses exist, with the EDISON Project's Data Science Framework (EDISON-DSF) from the European Union being the most complete. The EDISON-DSF includes both a Data Science Body of Knowledge (DS-BoK) and Competency Framework (CF-DS). This paper takes a critical look at how EDISON's CF-DS compares to recent work and other published curricular or …

Deep Learning-Based Technique For The Perception Of The Cervical Cancer, Aya Haraz, Hossam El-Din Moustafa, Abeer Twakol Khaleel, Ahmed H. Eltanboly

Mansoura Engineering Journal

In third-world countries, cervical cancer is the most prevalent and leading cause of death. It is affected by a variety of factors, including smoking, poor nutritional status, immunological inadequacy, and prolonged use of contraception. The Pap smear test, which is intended to prevent cervical cancer, finds preneoplastic changes in cervical epithelial cells. This study framework classified cervical cancer cells from Pap smears into five specified cell types using machine learning-based classification algorithms. The SIPaKMeD database is used in this investigation. This public dataset, which was manually cropped from 966 cluster cell images taken from Pap smear slides, has 4045 isolated …

Novel Feature Evaluation In Ultra-High Dimensional Right-Censored Data, With Applications To Head And Neck Cancer, Atika Farzana Urmi

Graduate Research Posters

Background: Head and neck cancer is the 6th most common cancer worldwide with an expected 1.08 million new cases each year. Such cancer data are ultra-high dimensional with thousands of clinical features and gene expressions, making it challenging for the traditional analytical tools to extract the potential biomarker for the cancer survival and control false discoveries. In addition, presence of heavy censoring can affect the screening procedures based on Kaplan-Meier (K-M) survival estimates.

Aim: To propose a model free, ultra-high dimensional feature screening method with two-dimensional survival outcome allowing false discovery rate (FDR) control.

Method: 516 primary tumor patients with …

Meta-Analysis Of Scent Detection Canines And Potential Factors Influencing Their Success Rates, Molly Marie Jaskinia

Graduate Student Theses, Dissertations, & Professional Papers

Objective: This is a meta-analysis focused on the success rates of scent detection canines and potential factors that could influence their accuracy. A series of statistical analyses were conducted to determine if certain demographic factors, such as the dog’s gender, age, and breed, have an effect on a scent dog’s accuracy during a search. Or if more circumstantial factors, like the dog’s level of experience in scent work, the type of target scent, and their handler’s awareness of the target’s location, affect the outcome of the search.

Materials and Methods: A dataset was created from 37 different articles consisting of …

Applications Of Transfer Learning From Malicious To Vulnerable Binaries, Sean Patrick Mcnulty

Graduate Student Theses, Dissertations, & Professional Papers

Malware detection and vulnerability detection are important cybersecurity tasks. Previous research has successfully applied a variety of machine learning methods to both. However, despite their potential synergies, previous research has yet to unite these two tasks. Given the recent success of transfer learning in many domains, such as language modeling and image recognition, this thesis investigated the use of transfer learning to improve vulnerability detection. Specifically, we pre-trained a series of models to detect malicious binaries and used the weights from those models to kickstart the detection of vulnerable binaries. In our study, we also investigated five different data representations …

Poisson Regression Model With Application To Wastewater Surveillance Under A Threshold Linear Mixed Model For Covid-19 Sensitivity Rates, Norou Diawara, Hueiwang Anna Jeng, Kyle Curtis, Raul Gonzalez, Nancy Welch, Cynthia Jackson, Rekha Singh, David Jurgens, Sasanka Adikari, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

A Threshold Linear Mixed Model (TLMM) has been developed to identify specific thresholds based on wastewater SARS-CoV-2 viral concentrations, which reflect COVID-19 cases. The thresholds can guide decisions regarding public health responses and prevention measures. To assess the practical application of TLMM, a simple simulation was conducted using a sample size of 100 and 500 replications. The simulation allowed for comparing parameter estimators by assessing bias and standard deviation and the root of the mean square error. The model and estimation procedures were applied to reported wastewater and clinic data to test its application for real-world scenarios. Our results demonstrated …

Utilizing Markov Chains To Estimate Allele Progression Through Generations, Ronit Gandhi

Honors Program: Senior Projects (Public)

All populations display patterns in allele frequencies over time. Some alleles cease to exist, while some grow to become the norm. These frequencies can shift or stay constant based on the conditions the population lives in. If in Hardy-Weinberg equilibrium, the allele frequencies stay constant. Most populations, however, have bias from environmental factors, sexual preferences, other organisms, etc. We propose a stochastic Markov chain model to study allele progression across generations. In such a model, the allele frequencies in the next generation depend only on the frequencies in the current one.

We use this model to track a recessive allele …

Evaluating Ai Sentiment Analysis, Aakriti Shah

Honors Program Theses

This paper presents a comparative analysis of human and AI performance on a sentiment analysis task involving the coding of qualitative data from community program transcripts. The results demonstrate promising but imperfect agreement between two AI models, Claude and Bing, versus three human annotators and one expert annotator using the Community Capitals framework categories. While both models achieved fair alignment with human judgment, confusion patterns emerged involving metaphorical language and text overlapping multiple categories. The findings provide a case study for benchmarking conversational AI systems against human baselines to reveal limitations and target improvements. Key gaps center around distinguishing between …