MATH 526 — Lifetime Data Analysis

In-depth study of survival analysis, covering foundational concepts and advanced techniques in time-to-event data analysis. Exploration of censoring and truncation, survival and hazard functions, and nonparametric methods like the Kaplan-Meier estimator. Core topics include hypothesis testing for survival distributions, parametric and semiparametric modeling, and covariate inclusion through the Cox proportional hazards model. Emphasis is placed on model diagnostics, validation, and variable selection techniques, including best-subset selection, LASSO, and nonconcave penalized likelihood approaches. Practical applications and hands-on analysis using R for survival data in research and applied settings.

• Credits: 4
• Faculty: Faculty of Science
• Department: Mathematics and Statistics
• Prerequisites: MATH 208 and (MATH 324 or MATH 357).