Statistical Models Commons^™

Calculation And Statistical Analysis Of Wins Above Replacement, Joshua Taylor

Departmental Honors & Graduate Capstone Projects

The Wins Above Replacement (WAR) statistic in Major League Baseball is a prominent metric used to estimate player value by quantifying all aspects of play in terms of wins added to a baseball team. We will use R to calculate WAR for all players from 1871 to 2012 and use data from those years to construct multivariate predictive models to attempt to estimate WAR for players from 2013 to 2024. We find strong correlations between predicted and actual WAR values for most models, with the exception of the polynomial predictive model for non-qualified pitchers.

Unlocking The Power Of Data: Enhancing Public Policy Through Advanced Data Infrastructure And Language Model Analysis, Zahid Asghar

CBER Conference

Data is the fundamental building block for advancements in artificial intelligence (AI), general AI (GAI), machine learning (ML), and large language models (LLMs). This study emphasizes the critical need for robust data infrastructure, arguing that without it, countries cannot fully benefit from technological advancements in various economic sectors. Governments possess vast repositories of both structured and unstructured data across multiple domains such as the judiciary, parliaments, and civil bureaucracy. However, these potential goldmines remain untapped due to inadequate data management capabilities and a lack of appreciation for the necessity of high-quality data. The research identifies key issues in public data …

The Energy Efficiency Price Premium Of Residential Buildings In Three Italian Regions, Elena Giarda, Demetrio Panarello

CBER Conference

The aim of this paper is to investigate whether a higher energy efficiency of residential buildings translates into higher house prices in Italy. We employ novel, and almost unexploited, data on Energy Performance Certificates of three Italian regions (EmiliaRomagna, Lombardy and Piedmont) and merge them with house prices and socioeconomic variables at various aggregation levels. The relationship between house prices and energy efficiency is estimated by means of hedonic regression models, quantile regressions and fixed effects panel data models. Our results reveal the existence of an energy-efficiency price premium in the three regions, with significant differences among them. Heterogeneity is …

Optimization Of Markov Chain Modeling In Predicting College Student Retention, Kien Nguyen

Journal of Global Education and Research

College student retention is one of the most important metrics in higher education. With institutions across the US facing decreasing enrollment, developing a reliable retention prediction method is crucial. In recent years, the use of the Markov chain model in forecasting student enrollment and progression has become more common, but there is little work on its application in student retention. One key factor in determining this model's effectiveness is what parameters should be used in the student population’s segmentation or grouping. This study presents a rigorous algorithm, coupled with a prediction model, capable of selecting parameters that provide the most …

Performance Of Acoustic Telemetry And Space Use Of Pallid Sturgeon In The Lower Platte River, Nebraska, Christopher F. Pullano

School of Natural Resources: Dissertations, Theses, and Student Research

Pallid Sturgeon (Scaphirhynchus albus) are centenarian, potamodromous, rheophiles that historically occupied the Missouri River and Mississippi River basins. Listed on the U.S. Endangered Species Act in 1990, population declines are attributed to habitat fragmentation and degradation, as well as overharvest, and hybridization. A knowledge gap exists regarding the extent to which tributaries facilitate key life stages for Pallid Sturgeon. This study evaluated the capacity of acoustic telemetry to monitor the movements of Pallid Sturgeon in a shallow, braided tributary to the Missouri River. The specific objectives were to (1) evaluate the environmental variables influencing the performance of acoustic …

Status Quo Of Large-Scale Models, Risks And Challenges, And Recommended Countermeasures, Le Cheng, Yang Xiao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Large-scale models (large models) are not only central to technological innovation, but also deeply entwined with national security, economic transformation, and social governance. This study examines the status quo of large-model development, identifies the key risks and challenges, and proposes response strategies, aiming to provide theoretical and policy insights for China’s navigations in global artificial intelligence (AI) competition and advances technological innovation. The research indicates that competition in the large-model market is fierce, while the industry is gradually consolidating. Competition in large models between China and the United States has escalated into a form of geopolitical contest. From a technical …

Poverty And Its Geospatial Association With Access To Hiv Treatment In Malawi, Zvifadzo Matsena Zingoni, Justin Okano, Joan Ponce, Luckson Dullie, Sally Blower

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

The Wallet And The Gut: Forecasting The 2024 Presidential Election With A State-By-State Adaptation Of The Time-For-Change Model, Simeon A. Betapudi, Hadassah Betapudi

Science University Research Symposium (SURS)

This study adapts Abramowitz's Time-for-Change model to a state-level framework to forecast the 2024 U.S. presidential election. The Time-for-Change model’s focus on the popular vote has become less relevant in recent years, given the growing divergence between popular vote outcomes and electoral college results. Our model addresses these issues by adapting the original Time-for-Change predictors (presidential approval rating, GDP, and time in office) to the state level. Using data from five election cycles (2004–2020), we employ an Ordinary Least Squares (OLS) regression to predict incumbent two-party vote share. Unlike the original model, state-level GDP and incumbency duration were found to …

Learning Problems Related To Stochastic Differential Equations, Jinpu Zhou

LSU Doctoral Dissertations

Stochastic differential equations (SDEs) are essential for modeling systems influenced by both deterministic dynamics and random fluctuations, with applications in a wide variety of disciplines. This thesis develops a Bayesian framework for nonparametric learning in SDEs, addressing key challenges in inference, particularly when dealing with complex systems and incomplete data. The thesis begins by establishing a theoretical foundation in optimization over Hilbert spaces, including a generalized representer theorem to address infinite-dimensional optimization problems encountered in nonparametric inference. Building on this, we introduce a Bayesian framework with shrinkage priors to learn drift functions from high-frequency data. Bayesian approach incorporates low-cost sparse …

Development Trends Of Large Models And Tencent’S Independent Innovation Practice, Jason Si

Bulletin of Chinese Academy of Sciences (Chinese Version)

The article discusses the emerging trends and application prospects of current large models, using Tencent’s Hunyuan large model as an example. It focuses mainly on innovations and implementations of large models in China. Companies like Google, Meta, and OpenAI have launched powerful models such as Google’s Gemini and Meta’s Llama 3, which have made significant progress in multi-modal applications and reasoning capabilities. China’s large models have significantly improved performance and efficiency by adopting the MoE (Mixture of Experts) architecture. Specifically, with its self-developed MoE trillion-parameter large model and deep learning framework, Tencent has made breakthrough advancements in large model technology …

Green Synthesis Of Carbonized Chitosan-Fe3o4-Sio2 Nano-Composite For Adsorption Of Heavy Metals From Aqueous Solutions, Dalia A. Ali Eng, Rinad Galal Ali Eng.

Chemical Engineering

Water pollution with heavy metals owing to industrial and agricultural activities have become a critical dilemma to humans, plants as well as the marine environment. Therefore, it is of great importance that the carcinogenic heavy metals present in wastewater to be eliminated through designing treatment technologies that can remove multiple pollutants. A novel green magnetic nano-composite called (Carbonized Chitosan-Fe3O4-SiO2) was synthesized using Co-precipitation method to adsorb a mixture of heavy metal ions included; cobalt (Co2+), nickel (Ni2+) and copper (Cu2+) ions from aqueous solutions. The novelty of this study was the synthesis of a new

nano-composite which was green with …

A Machine Learning Based Approach For The Identification Of Fake Bills, Tianyang Lu, Hongyang Pang

Rose-Hulman Undergraduate Mathematics Journal

Fake or counterfeiting currency, which has been around as long as money has existed, is a major economic problem. Since the US dollar is the most popular form of currency globally, it is the most popular currency to counterfeit. The United States Department of Treasury estimates that between $70 million and $200 million in fake bills are in circulation. The Federal Reserve Bank uses special banknote processing systems to count each bill deposited by the bank and examine them for the possibility of counterfeits. These machines have sensors designed to detect general quality of the bills, including paper type, quality …

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Sparse Neural Network To Enhance Performance Under Limited Parameter Constraints., Nailah Rawnaq

Graduate Theses and Dissertations

Over the past decade, the widespread adoption of deep neural networks has been a breakthrough driven by significant computational advancements. Additionally, the number of parameters of those models is exponentially increasing for performing complex tasks and achieving better performance. However, in most practical cases, often there are constraints in the number of parameters due to limited resources in storage size and computational cost. Network pruning can lead to an optimal solution to this problem. In this thesis, I present supporting evidence to the hypothesis that higher sparsity leads to better performance for a convolution-based neural network. I perform performance studies …

Exploring The Diagnostic Potential Of Radiomics-Based Pet Image Analysis For T-Stage Tumor Diagnosis, Victor Aderanti

Electronic Theses and Dissertations

Cancer is a leading cause of death globally, and early detection is crucial for better

outcomes. This research aims to improve Region Of Interest (ROI) segmentation

and feature extraction in medical image analysis using Radiomics techniques

with 3D Slicer, Pyradiomics, and Python. Dimension reduction methods, including

PCA, K-means, t-SNE, ISOMAP, and Hierarchical Clustering, were applied to highdimensional features to enhance interpretability and efficiency. The study assessed the ability of the reduced feature set to predict T-staging, an essential component of the TNM system for cancer diagnosis. Multinomial logistic regression models were developed and evaluated using MSE, AIC, BIC, and Deviance …

Gan With Skip Patch Discriminator For Biological Electron Microscopy Image Generation, Nishith Ranjon Roy

Graduate Theses and Dissertations

GAN models have been successfully used for image generation in various sections such as real-life objects like human faces, cars, animal faces, landscapes, etc. This work focuses on biological electron microscopy (EM) image generation. Unlike other real-life objects, biological EM images are obtained through electron microscopy techniques to study biological specimens. Electron microscopy offers high resolution and magnification capabilities, making it a powerful tool for visualizing biological structures at the nanoscale. However, using GAN models for biological EM image generation poses challenges due to the complex and unique arrangements of biological structures and the sparse and asymmetrical patterns in EM …

Forecasting Commercial Vehicle Miles Traveled (Vmt) In Urban California Areas, Steve Chung, Jaymin Kwon, Yushin Ahn

Mineta Transportation Institute

This study investigates commercial truck vehicle miles traveled (VMT) across six diverse California counties from 2000 to 2020. The counties—Imperial, Los Angeles, Riverside, San Bernardino, San Diego, and San Francisco—represent a broad spectrum of California’s demographics, economies, and landscapes. Using a rich dataset spanning demographics, economics, and pollution variables, we aim to understand the factors influencing commercial VMT. We first visually represent the geographic distribution of the counties, highlighting their unique characteristics. Linear regression models, particularly the least absolute shrinkage and selection operator (LASSO) and elastic net regressions are employed to identify key predictors of total commercial VMT. LASSO regression …

Bayesian Approaches In Multi-State Markov Models And High Dimensional Time-To-Event Data., Yuchen Han

Electronic Theses and Dissertations

This dissertation consists of two projects. The first one involves nonparametric methods on Continuous Time Markov Chains (CTMCs). The second one is centered around Bayesian shrinkage models for detecting prognostic and predictive biomarkers in high-dimensional clinical data. Both these projects build on methods from across the frequentist and Bayesian paradigm to offer novel solutions. In the first project, we aim to model the nonlinear effects of continuous variables within multistate framework in a non-parametrically by appealing to the rich mathematical framework of Reproducing Kernel Hilbert Spaces (RKHS). Then we adapted the classical Representer Theorem to penalized (squared norm) log-likelihood which …

Monetary Policy Impact On Stock Returns For Selected South Asian Countries, Neluka Devpura, Paresh Kumar Narayan, Navin Perera

Bulletin of Monetary Economics and Banking

In this paper, we examine the monetary policy impact on the stock market returns and volatility for four major South Asian countries (Bangladesh, India, Pakistan, and Sri Lanka). We test our hypothesis that monetary policy influences both the first and second order of stock returns by using monthly data. The short-term interest rate and the Treasury bill rate are employed as proxies for monetary policy. Controlling for industrial production, inflation, exchange rates (vis-à-vis the US dollar), US interest rate, and money supply, our findings indicate that there exists a statistically significant impact of short-term interest rates on stock returns only …

Design And Evaluation Of An Esa-Based Method Of Ensemble Subsetting For A Wofs (Warn On Forecast-Like System), Daniel J. Butler

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Forecasting severe thunderstorm environments in the southeastern United States can be challenging due to mesoscale heterogeneities such as shortwave troughs, pre-existing airmass boundaries, cold fronts aloft, low-level jets, dry air intrusions, and mesoscale lows. To combat these challenges, ensemble sensitivity analysis (ESA) may be applied to a Warn-on-Forecast (WOF)-like ensemble to improve forecasts of severe convection through ensemble weighting and subsetting. Ensemble-based weighting and subsetting uses ensemble members that most accurately represent the thunderstorm environment in areas of mesoscale heterogeneity. This study creates and evaluates the ensemble-based weighting and subsetting in four cases of severe thunderstorm occurrence. The open parameter …