Statistical Methodology Commons^™

Supplemental Bibliographic Details. From 2001 Mars Odyssey To Earth’S Climate Crisis: Integrating Gamma Spectroscopy, Martian Soil Simulants, And Plant Genomes For Agroecology, Anchored In Sri Lanka’S Mars-Context Serpentinites, Suniti Karunatillake, Maheshi Dassanayake, Carlos Gary Bicas

Planetary Science Lab

Bibliographic details follow to supplement hyperlinked citations in the multinational GANGOTRI-supporting project conceived by Karunatillake, Dassanayake, and Gary-Bicas

Interpretable Sample Uncertainty Measures For Ranked-Choice Election Polls, Jason Liang

CMC Senior Theses

Polling results from traditional single-choice plurality elections are readily interpretable. Simple frequentist population parameters are estimated, including each candidate’s total support and the size of the front runner's lead. If the point estimate for the size of the front runner's lead exceeds the margin of error of the lead, we can conclude that the poll shows a statistically significant front runner. However, the interpretability of these population statistics disappears when applied to ranked-choice voting elections. Because ballots rank multiple candidates and candidates are eliminated in rounds, simple population-wide parameters are not well-defined. In RCV elections, a candidate’s ability to win …

Bayesian Analysis Of Nominal Outcomes With Missing Values Using Multinomial And Multivariate Multinomial Probit Models, Suwash Silwal

Dissertations, Master's Theses and Master's Reports

Nominal outcomes frequently arise in health sciences, transportation, economics, market research, and related fields. These data often contain missing values, while longitudinal and panel studies generate multiple correlated nominal responses. Bayesian estimation of multinomial probit (MNP) and multivariate multinomial probit (MMNP) models provides a flexible framework for analyzing such data but remains computationally challenging due to high-dimensional likelihood integration, restrictive covariance identification constraints, and poor mixing of Markov chain Monte Carlo (MCMC) algorithms, particularly in the presence of missing data. This dissertation develops parameter-expanded data augmentation (PX-DA) methods for MNP and MMNP models with missing nominal outcomes by incorporating parameter …

Modeling Housing Prices: Which Features Matter Most?, Alex Ruvolo

Williams Honors College, Honors Research Projects

This paper attempts to find the biggest factors and traits that influence the cost of housing. This will include the lot size, type of street, utilities, neighborhood, year built, heating, electrical, yard size, number of different rooms, age, condition, and others. I will attempt to answer the question of whether the prices of houses have changed within the last 5 to 10 years, and obviously this is an easy question to answer. However, the bigger question beyond this is are the main factors affecting housing prices all important in explaining this relationship? Is one factor more important than the rest …

Hybrid Patchtst And Physics-Based Framework For Predicting Lithium-Ion Battery State Of Health, Pavan Ravuri

Honors Undergraduate Theses

Accurately forecasting the state of health of lithium-ion batteries is critical for improving performance, reliability and lifetime in energy storage applications. Battery capacity degrades into a nonlinear pattern over cycling due to electrochemical processes where neither purely data driven nor physics-based models can capture alone. This study looks at a hybrid framework combining that PatchTST patch-based transformer architecture with physics-based features derived from the solid electrolyte interphase and pseudo two-dimensional models. Physics inspired proxy features were computed from cycling data and concatenated with electrochemical measurements as added input channels before patch segmentation. There are five model configurations that were evaluated …

Machine Learning-Based Spatio-Temporal Modeling Of Climate Dynamics And Desertification In The Sahara–Sahel Region, Stephen M. Tivenan

Theses and Dissertations

Arid climate classifications are threshold-dependent and easily interpretable mappings that are widely used in ecological, agricultural, and climate-related studies. These classifications inform scientific understanding, support policy and land management decisions, and provide an intuitive summary of environmental conditions. Despite their usefulness, traditional arid climate classifications often fail to quantify uncertainty, incorporate spatial context, or account for complex relationships among relevant environmental variables. Existing approaches to uncertainty assessment have largely relied on comparing classifications across multiple datasets or alternative formulas, but these methods generally overlook important spatial dependence and latent structure in the data.

This dissertation develops three machine learning-based statistical …

A Geospatial Assessment Of Groundwater Salinization In A Multi-Aquifer System: Durango, Mexico, Juan Lopez-Sierra

Graduate Theses/Dissertations

Groundwater salinization poses a critical environmental concern for water resource sustainability in arid and semi-arid regions. This study evaluates spatial and temporal patterns of groundwater salinity across the state of Durango, Mexico, using total dissolved solids (TDS), sodium adsorption ratio (SAR), as salinity indicators and nitrate-nitrogen (NO₃–N) as an anthropogenic indicator. Groundwater quality data were obtained from (CONAGUA), a Mexican water agency. To assess salinity variations with respect to time, while minimizing interannual sampling bias, two multi-year sampling periods were selected: 2012-2013, and 2020-2021. Final datasets consisted of 122 wells for 2012–2013 and 131 wells for 2020–2021. The wells were …

An Integrated Data-Driven Framework For Arctic Shipping: Analyzing Vessel Speed, Environmental And Ecological Factors Through Innovative Statistical Spatio-Temporal Methods, Inverse Optimization And Machine Learning, Mauli Pant

Theses and Dissertations

This dissertation develops an integrated data-driven framework to analyze vessel navigation and ecological risk in the United States Arctic from 2010 to 2019. As environmental change and maritime activity increase in the region, understanding how vessels respond to dynamic conditions and how those responses interact with marine ecosystems has become increasingly important. A central theme of this dissertation is the treatment of vessel speed as both an observed outcome and a decision variable reflecting trade- offs among operational, environmental, and ecological factors. The first chapter develops a predictive framework for vessel speed over ground (SOG) using Gaussian Process Boosting (GPBoost), …

Statistical Quality Control: A Bayesian Framework, Jakia Jaber Tunal

College of Graduate Studies: Theses & Dissertations

In many industries, it is important to assess whether a machine or system is operating within acceptable limits or has gone out of control. This project applies Bayesian statistics to monitor a process over time and detect changes in its behavior. First, initial data are collected to understand the system’s typical performance and to form a starting prior distribution. As new observations arrive over time, the prior is updated through Bayesian inference, combining past information with incoming data. This iterative updating creates a continuous monitoring framework that adapts as more evidence becomes available. When the updated results suggest that the …

Major And Institution Transfer Path Combinations And The Relaxed Lasso Method: Parsimonious Prediction Of Vertical Transfer Student Success, David Wutchiett, A. W. Logue, Martin Kurzweil, Colin Chellman

Publications and Research

The majors, institutions, and their combinations, which characterize transfer between higher education institutions relate to student success but introduce high-dimensionality and multicollinearity to statistical modeling. Relaxed lasso regression, a procedure blending regularized lasso models with unregularized refit models, selects variables and facilitates unbiased coefficient estimation in high-dimensional settings. Using a large sample of students transferring from associate to bachelor’s programs (n=28,166), we compared lasso and ridge regression parsimonious models predicting bachelor’s graduation and posttransfer GPA improvement, evaluating associations using unregularized parsimonious predictive models. Relaxed lasso parsimonious optimal models outperformed standard lasso and ridge regression models while including few of 702 …

Flavor, Transverse Momentum, And Azimuthal Dependence Of Charged Pion Multiplicities In Semi-Inclusive Deep-Inelastic Scattering With 10.6 Gev Electrons, P. Bosted, H. Bhatt, S. Jia, W. Armstrong, D. Dutta, R. Ent, D. Gaskell, E. Kinney, H. Mkrtchyan, S. Ali, R. Ambrose, D. Androic, C. Ayerbe Gayoso, A. Bandari, V. Berdnikov, D. Bhetuwal, D. Biswas, M. Boer, E. Brash, A. Camsonne, M. Cardonna, J. P. Chen, J. Chen, M. Chen, E. M. Christy, S. Covrig, S. Danagoulian, M. Diefenthaler, B. Duran, C. Elliot, H. Fenker, E. Fuchey, J. O. Hansen, F. Hauenstein, T. Horn, G. M. Huber, M. K. Jones, M. L. Kabir, A. Karki, B. Karki, S. J. D. Kay, C. Keppel, V. Kumar, N. Lashley-Colthirst, W. B. Li, D. Mack, S. Malace, P. Markowitz, M. Mccaughan, E. Mcclellan, D. Meekins, R. Michaels, A. Mkrtchyan, C. Morean, G. Niculescu, I. Niculescu, B. Pandey, S. Park, E. Pooser, B. Sawatzky, G. R. Smith, H. Szumila-Vance, A. S. Tadepalli, V. Tadevosyan, R. Trotta, H. Voskanyan, S. A. Wood, Z. Ye, C. Yero, X. Zheng, Hall C Sidis Collaboration

Physics Faculty Publications

Measurements of semi-inclusive deep-inelastic scattering multiplicities for 𝜋⁺ and 𝜋⁻ from proton and deuteron targets are reported on a grid of hadron kinematic variables 𝑧, 𝑃𝑇, and 𝜙* for leptonic kinematic variables in the range 0.3< 𝑥< 0.6 and 3< 𝑄²< 5GeV². Data were acquired in 2018 and 2019 at Jefferson Lab Hall C with a 10.6 GeV electron beam impinging on 10-cm-long liquid hydrogen and deuterium targets. Scattered electrons and charged pions were detected in the High Momentum Spectrometer and Super High Momentum Spectrometer, respectively. The multiplicities were fitted for each bin in (𝑥,𝑄²,𝑧,𝑃_𝑡) to extract the 𝜙*—independent 𝑀₀ and the azimuthal modulations ⟨cos⁡(𝜙*)⟩ and ⟨cos⁡(2⁢𝜙*)⟩. The 𝑃_𝑡 dependence of the 𝑀₀ results was found to be remarkably consistent for the four cases studied: 𝑒⁢𝑝→𝑒⁢𝜋⁺⁢𝑋, 𝑒⁢𝑝→𝑒⁢𝜋^−⁢𝑋, 𝑒⁢𝑑→𝑒⁢𝜋⁺⁢𝑋, 𝑒⁢𝑑→𝑒⁢𝜋^−⁢𝑋 over the range 0GeV< 𝑃_𝑡< 0.4GeV, as were the multiplicities evaluated near 𝜙*=180∘ over the extended range 0GeV< 𝑃_𝑡< 0.7GeV. The Gaussian widths of the 𝑃𝑡 dependence exhibit a quadratic increase with 𝑧. The cos⁡(𝜙*) modulations were found to be consistent with zero for 𝜋⁺, in agreement with previous world data, while the 𝜋⁻ moments were, in many cases, significantly greater than zero. The cos⁡(2⁢𝜙*) modulations …

A Comparative Evaluation Of Data Imbalance Handling Techniques In Machine Learning Models For One-Year Mortality Prediction In Liver Cirrhosis, Sumiya Hasan Trisha

UNF Graduate Theses and Dissertations

Liver cirrhosis is associated with substantial morbidity and mortality, making one-year mortality prediction a clinically relevant problem. Using a liver cirrhosis dataset as the motivating application, this thesis evaluates five machine learning classifiers—Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost—under five class-imbalance handling strategies: Baseline learning, Random Oversampling, SMOTE-NC, ADASYN, and Cost-Sensitive Learning. Hyperparameter tuning was conducted using randomized search, and predictive performance was assessed over 200 iterations of Monte Carlo Cross-Validation using Accuracy, Precision, Recall, Fl-score, and ROC-AUC.

The results suggest that imbalance-handling strategies can materially affect predictive performance, particularly recall. Because the outcome of interest is death within …

Variance Shrinkage In Dunnett-Type Multiple Comparisons With Missing Data, Md Habibullah

UNF Graduate Theses and Dissertations

Dunnett’s procedure is widely used for comparing multiple treatments with a control, but its application becomes challenging in the presence of missing data and multiple com- parisons. An improved Dunnett-type procedure addresses this by using multiple imputation under Rubin’s framework and constructing unified confidence intervals based on a multi- variate t distribution, allowing valid simultaneous inference while controlling the family-wise error rate (FWER). This work further extends the method by incorporating shrinkage-based variance estimation. Specifically, individual group variances are shrunk toward a common value to improve stability. This approach is particularly effective when group variances are similar or moderately different, …

Ridit-Based Adaptive Allocation In Two-Stage Clinical Trials With Binary Outcomes, Dewan Fahim

UNF Graduate Theses and Dissertations

This study explores better ways to assign patients to treatments in clinical trials with binary outcomes, such as success or failure. Adaptive methods are used to learn from early results and adjust treatment assignments during the trial, helping more patients receive better performing treatments while maintaining reliable conclusions. We focus on trials comparing multiple treatments using a two-stage design. In the first stage, several treatments are tested to identify the most promising one; in the second stage, that treatment is compared with a control. Unlike traditional equal assignment, we use adaptive allocation in the second stage to make better use …

A Comparative Study Of Classification Methods For Healthcare Analytics, Xueting Zhao

UNF Graduate Theses and Dissertations

This thesis presents a comparative study of logistic regression, Linear Discriminant Analy- sis (LDA), and Quadratic Discriminant Analysis (QDA) for binary classification in healthcare analytics, integrating theoretical derivation, simulation, and real-data application. A facto- rial simulation study crosses the covariance structure (equal vs. unequal), predictor correla- tion (ρ ∈ {0, 0.5, 0.9}), dimensionality (p ∈ {2, 5, 10}) and sample size (n ∈ {50, 100, 200}) across 54 scenarios with 1,000 Monte Carlo replicates each. Three main findings emerge. Logistic regression and LDA are nearly interchangeable when the assumption of equal-covariance holds. QDA achieves substantially better discrimi- nation when class-specific …

Two-Stage Response-Adaptive Randomization Designs For Multi-Arm Trials With Normal Outcome, Tanjin Tamanna Happy

UNF Graduate Theses and Dissertations

This study focuses on improving how clinical trials compare new treatments with a standard treatment, when the response is quantitative (normally distributed). In a common two-stage design, several new treatments are first evaluated, and the best-performing one is selected if it appears better than the standard. In the second stage, this selected treatment is compared again with the standard using additional data to confirm its effectiveness. This approach is known to be efficient in terms of accuracy and sample size savings. We extend this design by introducing an adaptive method for assigning patients to treatments in the second stage. Instead …

Numerical Simulation Analysis On Support Performance Of Inclined-Vertical Combined Double-Row Interlocked Steel Pipe Piles, Zhou Yi, Zhang Ning, Wang Yanhua, Zhou Dequan

Journal of China & Foreign Highway

To select appropriate support structures in foundation pit engineering of underground tunnels for the Hong Kong International Airport (HKIA ),this paper proposed the use of inclined-vertical combined interlocked steel pipe piles,which are beneficial for support,water isolation,and recovery.Numerical simulations were conducted using finite element software Midas GTS NX to analyze the effect of different combination types and inclination angles on the engineering response of the inner-row piles of inclined-vertical combined double-row interlocked steel pipe piles.The results show that the inclined-vertical combined double-row piles reduce the horizontal displacement at the top of the inner-row piles by approximately 50% and decrease the peak …

Influence Mechanism Of Static And Dynamic Loading On Adsorbed Bound Water And Deformation In High Liquid Limit Soils, Fang Zhongwang, Zhou Shiji E, Xiao Yupeng, Peng Xin, Zhang Rui

Journal of China & Foreign Highway

In order to reveal the evolution law of adsorbed bound water and the deformation characteristics of high liquid limit soil under static and dynamic loading,Hainan high liquid limit soil and Hunan clayey sand were selected as comparative samples.Through consolidation,consolidation-creep,and dynamic triaxial tests,the deformation patterns of high liquid limit soil under different loading conditions were obtained.By combining nuclear magnetic resonance tests,the variation pattern of adsorbed bound water content in specimens before and after the consolidation and dynamic triaxial tests was studied.The results show that under static loading,the compression coefficient of high liquid limit soil meets the requirements of the Specifications for …

Utilization Technology And Compaction Quality Control Of Hardened Layer In Beam Yard Of Rock-Filled Roadbed, Cheng Xiaoliang, Tian Wendi, Gao Mengke, Zhou Xujia

Journal of China & Foreign Highway

To investigate the utilization technology and compaction quality control of hardened layers in the beam yard of rock-filled roadbed,the optimization design and construction control methods for rock-filled roadbed under different conditions were proposed based on a highway project in Guangdong Province.When the elevation of the hardened layer was lower than the top surface elevation of the roadbed and the top surface bending and settlement value of the hardened layer was less than that of the rock-filled roadbed,the gravel material could be filled directly on the hardened layer to the top surface elevation of the roadbed,and acceptance and pavement design could …

Shaking Table Test And Numerical Simulation Of Seismic Response Characteristics Of Rock Slope With Muddy Interlayer, Duan Chenggang, Fang Yanlin, Tian Yeqing, Zhou Zhijun

Journal of China & Foreign Highway

In order to study the seismic macroscopic failure process and dynamic response characteristics of rock slope with muddy interlayer and reveal the influence of muddy interlayer on slope stability,a similar slope model with concave and convex slope types was designed,and shaking table test was carried out;finite element analysis was used for numerical modeling analysis,and the results obtained by the two models were compared.The results show that:① The macroscopic deformation and failure process of the model slope is divided into three stages.Muddy interlayer dislocates,and cracks occur;mud interlayer slips,and cracks expand;slope collapses;② The seismic load has a decisive influence on the stability …