STAT 3112 – Statistical Inference II

About Course

Statistical Inference II is an advanced course that builds on the foundational concepts of statistical inference. Students learn about advanced topics in hypothesis testing, estimation, and regression analysis, as well as advanced inference methods such as maximum likelihood estimation and asymptotic theory. The subject emphasizes the application of statistical methods to real-world problems in fields such as medicine, engineering, and social sciences. By studying Statistical Inference II, students gain a deeper understanding of statistical concepts, advanced techniques for data analysis, and the ability to make sound statistical decisions.

Advanced techniques for model selection, including information criteria, cross-validation, and Bayesian model selection.
Theory and application of advanced inference methods, such as maximum likelihood estimation, asymptotic theory, and decision theory.
Robust estimation and nonparametric methods, which are useful for analyzing data that does not meet certain assumptions.
Generalized linear models, which are a flexible and widely used framework for modeling various types of data.

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