Statistics and Probability Commons^™

A Comparison Of Five Statistical Methods For Predicting Stream Temperature Across Stream Networks, Maike F. Holthuijzen

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The health of freshwater aquatic systems, particularly stream networks, is mainly influenced by water temperature, which controls biological processes and influences species distributions and aquatic biodiversity. Thermal regimes of rivers are likely to change in the future, due to climate change and other anthropogenic impacts, and our ability to predict stream temperatures will be critical in understanding distribution shifts of aquatic biota. Spatial statistical network models take into account spatial relationships but have drawbacks, including high computation times and data pre-processing requirements. Machine learning techniques and generalized additive models (GAM) are promising alternatives to the SSN model. Two machine learning …

Developing Biomarker Combinations In Multicenter Studies Via Direct Maximization And Penalization, Allison Meisner, Chirag R. Parikh, Kathleen F. Kerr

UW Biostatistics Working Paper Series

When biomarker studies involve patients at multiple centers and the goal is to develop biomarker combinations for diagnosis, prognosis, or screening, we consider evaluating the predictive capacity of a given combination with the center-adjusted AUC (aAUC), a summary of conditional performance. Rather than using a general method to construct the biomarker combination, such as logistic regression, we propose estimating the combination by directly maximizing the aAUC. Furthermore, it may be desirable to have a biomarker combination with similar predictive capacity across centers. To that end, we allow for penalization of the variability in center-specific performance. We demonstrate good asymptotic properties …

Gridiron-Gurus Final Report: Fantasy Football Performance Prediction, Kyle Tanemura, Michael Li, Erica Dorn, Ryan Mckinney

Computer Science and Software Engineering

Gridiron Gurus is a desktop application that allows for the creation of custom AI profiles to help advise and compete against in a Fantasy Football setting. Our AI are capable of performing statistical prediction of players on both a season long and week to week basis giving them the ability to both draft and manage a fantasy football team throughout a season.

Predicting Future Years Of Life, Health, And Functional Ability: A Healthy Life Calculator For Older Adults, Paula Diehr, Michael Diehr, Alice M. Arnold, Laura Yee, Michelle C. Odden, Calvin H. Hirsch, Stephen Thielke, Bruce Psaty, W Craig Johnson, Jorge Kizer, Anne B. Newman

UW Biostatistics Working Paper Series

Introduction

Planning for the future would be easier if we knew how long we will live and, more importantly, how many years we will be healthy and able to enjoy it. There are few well-documented aids for predicting our future health. We attempted to meet this need for persons 65 years of age and older.

Methods

Data came from the Cardiovascular Health Study, a large longitudinal study of older adults that began in 1990. Years of life (YOL) were defined by measuring time to death. Years of healthy life (YHL) were defined by an annual question about self-rated health, and …

Longitudinal Measurement And Hierarchical Classification Framework For The Prediction Of Alzheimer's Disease, Meiyan Huang, Wei Yang, Qianjin Feng, Wufan Chen, Michael Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack Jr., William Jagust, John Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew Saykin, John Morris, Leslie M. Shaw, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro, Bryan Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Charles D. Smith, Greg A. Jicha, Peter A. Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad

Neurology Faculty Publications

Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important …

A Bayes Interpretation Of Stacking For M-Complete And M-Open Settings, Tri Le, Bertrand S. Clarke

Department of Statistics: Faculty Publications

In M-open problems where no true model can be conceptualized, it is common to back off from modeling and merely seek good prediction. Even in M-complete problems, taking a predictive approach can be very useful. Stacking is a model averaging procedure that gives a composite predictor by combining individual predictors from a list of models using weights that optimize a cross validation criterion. We show that the stacking weights also asymptotically minimize a posterior expected loss. Hence we formally provide a Bayesian justification for cross-validation. Often the weights are constrained to be positive and sum to one. For greater generality, …

Predictive Modeling Of Adolescent Cannabis Use From Multimodal Data, Philip Spechler

Graduate College Dissertations and Theses

Predicting teenage drug use is key to understanding the etiology of substance abuse. However, classic predictive modeling procedures are prone to overfitting and fail to generalize to independent observations. To mitigate these concerns, cross-validated logistic regression with elastic-net regularization was used to predict cannabis use by age 16 from a large sample of fourteen year olds (N=1,319). High-dimensional data (p = 2,413) including parent and child psychometric data, child structural and functional MRI data, and genetic data (candidate single-nucleotide polymorphisms, "SNPs") collected at age 14 were used to predict the initiation of cannabis use (minimum six occasions) by age 16. …