Graduate Statistics Problem Sets
I put my coursework from SIUe's Master's in Mathematics program up on the problem sets section of this site. Five courses from 2021-2022 that formed the core of my graduate statistics work. The Courses STAT 478 - Time Series Analysis Spring 2021, Dr. Beidi ARIMA models, forecasting, spectral analysis, state space methods. The problem sets mix theoretical derivations with R implementations for temporal data. STAT 482 - Regression Analysis Fall 2022, Dr. Andrew Neath Linear models, diagnostics, variable selection, model comparison. Neath's approach put the theoretical foundations front and center, with application following from understanding. STAT 575 - Computational Statistics Summer 2021, Dr. Qiang Beidi Probably the most hands-on course of the five. Topics included: Newton-Raphson and numerical optimization Monte Carlo simulation Sampling methods (inverse transform, acceptance-rejection) Hand-coded MLE for Poisson regression Implementing these algorithms from scratch instead of calling library functions teaches you what the methods are actually doing. You hit the edge cases. You debug convergence failures. That's where the understanding comes from. STAT 579 - Discrete Multivariate Analysis Spring 2021, Dr. Andrew Neath Categorical data analysis, log-linear models, contingency tables. Both the mathematical theory and R implementations for discrete multivariate data. STAT 581 - Statistical Methods Fall 2021, Dr. Neath Experimental design, ANOVA, general linear models with practical applications. Why Share This? A few reasons. Learning resource. Worked solutions for graduate statistics are surprisingly hard to find online. If someone studying this material stumbles across these and they help, good. Personal archive. I did much of this work during cancer treatment. Keeping it organized and accessible matters to me. Reference. I still look up my own derivations and implementations when something comes up in research. Easier to find them here than to dig through old dir