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Full-Text Articles in Categorical Data Analysis
Bayesball : A Comprehensive Framework For Predicting Ucl Injury, Brady M. Pinter, Will Best Ph.D.
Bayesball : A Comprehensive Framework For Predicting Ucl Injury, Brady M. Pinter, Will Best Ph.D.
SPARK Symposium Presentations
Ulnar Collateral Ligament (UCL) reconstruction, commonly referred to as Tommy John Surgery, has seen a significant rise among Major League Baseball (MLB) pitchers, prompting growing interest in identifying the mechanical and performance-based factors that contribute to injury risk. While previous studies have examined these relationships using traditional frequentist approaches separately, this study combines multiple different model techniques to present a broad framework for finding significant predictors of UCL Surgery. These models include Lasso and Ridge Regression, Principal Component Regression (PCR) , Partial Least Squares Regression (PLS) , Random Forest, Multiple Linear Regression, and a Bayesian Statistical Model. Using these models, …
Aleci: An R Package For Non-Parametric Confidence Intervals On Accumulated Local Effects Plots, Matthew R. Lister
Aleci: An R Package For Non-Parametric Confidence Intervals On Accumulated Local Effects Plots, Matthew R. Lister
All Graduate Reports and Creative Projects, Fall 2023 to Present
Machine learning models can take a collection of inputs and craft an output. The mathematical formulas these models use to calculate their outputs easily become too complex or time consuming for a human to analyze. Collectively, we refer to these as black box models. Accumulated local effects plots (ALE) are a method for adding interpretability and visibility into the effects that individual variables contribute to the predictions made by black box models. The method designed by D.W. Apley calculates equally spaced point estimates of the response value to construct a graph across the range of the variable of interest. AleCI …
Identifying Key Activity Indicators In Rats' Neuronal Data Using Lasso Regularized Logistic Regression, Avery Woods
Identifying Key Activity Indicators In Rats' Neuronal Data Using Lasso Regularized Logistic Regression, Avery Woods
Honors Theses
This thesis aims to identify timestamps of rats’ neuronal activity that best determine behavior using a machine learning model. Neuronal data is a complex and high-dimensional dataset, and identifying the most informative features is crucial for understanding the underlying neuronal processes. The Lasso regularization technique is employed to select the most relevant features of the data to the model’s prediction. The results of this study provide insights into the key activity indicators that are associated with specific behaviors or cognitive processes in rats, as well as the effect that stress can have on neuronal activity and behavior. Ultimately, it was …
Teaching Introductory Statistics With Datacamp, Benjamin Baumer, Andrew P. Bray, Mine Çetinkaya-Rundel, Johanna S. Hardin
Teaching Introductory Statistics With Datacamp, Benjamin Baumer, Andrew P. Bray, Mine Çetinkaya-Rundel, Johanna S. Hardin
Statistical and Data Sciences: Faculty Publications
We designed a sequence of courses for the DataCamp online learning platform that approximates the content of a typical introductory statistics course. We discuss the design and implementation of these courses and illustrate how they can be successfully integrated into a brick-and-mortar class. We reflect on the process of creating content for online consumers, ruminate on the pedagogical considerations we faced, and describe an R package for statistical inference that became a by-product of this development process. We discuss the pros and cons of creating the course sequence and express our view that some aspects were particularly problematic. The issues …
Visualizing Lab And Phenotype Associations Using Phewas And Electronic Health Records, Brenda Emerson, Miriam Goldman, Sahiti Kolli
Visualizing Lab And Phenotype Associations Using Phewas And Electronic Health Records, Brenda Emerson, Miriam Goldman, Sahiti Kolli
Honors Projects
As the digitization of patient health records is becoming more common, we are given a great opportunity to analyze these records and hopefully make discoveries about diseases or medicines. Being given large datasets of Electronic Health Records, I and two other students decided to look for novel phenotype associations with mean lab values, look to see whether the presence of a lab had associations with a phenotype, and create an interactive application to visual the associations between labs and phenotypes.