Search
Now showing items 181-190 of 212
Methods for Estimating the Health Effects of Exposure to Point Sources of Emissions Using Large-Scale and Diverse Data Sources
(2018-04-30)
There is a well-documented association between exposure to fine particulate matter (PM2.5) and numerous health outcomes, with some evidence suggesting PM2.5 originating from coal combustion may have different health impacts. ...
Nucleotide-Level Modeling of Genetic Regulation Using Dilated Convolutional Neural Networks
(2017-07-14)
The expression of genes is the product of a complex regulatory process, whose complete nature remains elusive. In order to better understand gene regulation, this work seeks to improve on efforts to model the locations of ...
Using Physiological Big Data to Predict Cross Country Performance
(2016-06-22)
Sleep quality and heart rate variability are hypothesized in research to be indicators of improved or impaired athletic performance. This is especially relevant for the sport of endurance running. Very little research has ...
But What Did You Actually Learn? Improving Inference for Non-Identifiable Deep Latent Variable Models
(2023-05-12)
Deep probabilistic and Bayesian latent variable models allow one to infer variables that have previously not been observed in the data in order to accurately model the data density. They provide an intuitive and flexible ...
What's Missing from Machine Learning for Medicine? New Methods for Causal Effect Estimation and Representation Learning from EHR Data
(2023-05-09)
This thesis explores the applications of deep learning in clinical and epidemiologic data analysis, focusing on neural networks for causal effect estimation and clinical risk prediction. I claim that neural networks have ...
A Gaussian-process Framework for Nonlinear Statistical Inference using Modern Machine Learning Models
(2023-05-12)
Gaussian Process Regression has become widely used in biomedical research in recent years, particularly for studying the intricate and nonlinear impacts of multivariate genetic or environmental exposures. This dissertation ...
On Causal Inference in Real World Settings
(2023-05-12)
In the present dissertation, we consider three classical and yet modern topics in causal inference -- surrogate markers, multi-source federated learning, and sensitivity analysis. In each case, present-day obstacles in ...
Contributions to Design and Analysis of Pediatric HIV Studies
(2023-05-11)
Youth with perinatal HIV exposure have demonstrated higher rates of emotional---behavioral problems than the general US youth population. However, more evidence is needed to help target prevention and intervention efforts ...
Game, Site, Match: Topics in Causal Inference and Sports Statistics
(2023-05-12)
This thesis presents three self-contained chapters: a dynamic linear model for rating athletes using their game scores (Game), new perspectives power analysis for multisite trials (Site), and a synthetic matching method ...
Modeling and Diagnostics for Paired Comparison Data and Rank Order Data
(2023-05-11)
Paired comparison models are used for analyzing data that involves pairwise comparisons among a set of objects. When the outcomes of the pairwise comparisons have no ties, the paired comparison models can be generalized ...