Methodology

This work is currently ongoing.

This work develops a novel framework combining reinforcement learning, drift-diffusion models, and hidden Markov models to analyze reward-based decision-making, response times, and strategy switching, with a focus on distinguishing individuals with Major Depressive Disorder from healthy controls and linking behavior to neuroimaging and clinical outcomes.

This work advances boosting methods by developing strategies to handle missing or interval-censored response variables, implementing them with functional gradient descent, establishing theoretical guarantees, and demonstrating strong finite-sample performance through numerical studies.

This work addresses challenges in missing data analysis by proposing a unified modeling framework that uses generalized additive models to handle various missing data mechanisms without requiring specific assumptions, while incorporating regularized likelihood for concurrent estimation and variable selection, with rigorous theoretical guarantees and demonstrated empirical performance.

This work investigates joint models for genetic association with longitudinal biomarkers and time-to-event outcomes, developing a closed-form sample size formula, using spline functions for robustness against non-linearity, and evaluating validity, sensitivity, and power through simulations and application to genetic data from the Diabetes Control and Complications Trial.