Publication Date

Summer 7-28-2026

Abstract

This dissertation analyzes one of the few publicly available NFL injury datasets to study field type and non-contact lower-limb injuries. Field type is studied jointly with other risk factors to understand how these factors interact to affect injury risk. The data were gathered through a case-control sampling scheme, which limits direct inference on absolute injury probabilities. While not the most common approach for case-control data, this dissertation models the retrospective distribution directly through Log-Linear General Location Models (Log-Linear GLOMs). Through a log-linear structure placed on a log-odds-ratio reparameterization, the model provides directly interpretable marginal and interaction contributions to injury log-odds ratios, while still allowing flexible modeling of problem-specific dependence structures. The model's properties are investigated through simulation studies before it is used to study potential injury risk factors in the 1st & Future dataset.

Degree Name

Statistics

Level of Degree

Doctoral

Department Name

Mathematics & Statistics

First Committee Member (Chair)

Fletcher Christensen

Second Committee Member

Gabriel Huerta

Third Committee Member

Yan Lu

Fourth Committee Member

Huining Kang

Language

English

Keywords

case-control, log-linear, injuries, football, multivariate, mixed data

Document Type

Dissertation

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