Teaching

​

I am very much interested in Statistical Education. Together with my colleagues in the department we have completely revised and modernized our curriculum.  I believe that our programs at the undergraduates and graduate level are the best in Israel and very competitive globally.  In the upcoming academic year (starting in the Fall of 2017) I will be teaching two of our graduate level courses (Probability and Statistical Inference) as well as an undergraduate course (Computational Statistics).

​

Probability Theory: The course covers probability spaces, random variables and elements, expectations and transforms, inequalities and concentration, and convergence of random variables (the latter is a broad topic that includes laws of large numbers, convergence in distribution, central limit theorems and more). This is rigorous graduate level course which introduces some measure theory. I use my own notes and refer students for extra reading in Probability and Random Process (by Grimmett and Stirzaker), Probability (by Karr) and my favorite The Theory of Probability (by Venkatesh). For more information click here. 

​

Statistical Inference: This course covers the fundamentals of statistical inference and some large sample theory. Concepts such as likelihood, sufficiency and ancillarity are discussed. Various methods for obtaining estimators, such as moment estimators, maximum likelihood estimators and general plug-in estimators are given. The large sample properties of these estimators are derived. We also discuss testing in detail. Rank statistics, U-statistics and goodness of fit statistics are introduced and briefly studied. I use my own notes and refer students for extra reading in Statistical Inference (by Casella and Berger), Mathematical Statistics (by Knight), and Theoretical Statistics (by Cox and Hinkely). Sometime I refer to additional sources as well. For more information click here.