These 4 lectures by Michal Fabinger cover a frequently used statistical method: maximum likelihood estimation. The concepts are introduced in an intuitive yet rigorous way. The topics include likelihood and conditional likelihood and their maximization, maximum likelihood estimation of linear models, logit models, models/logistic regression, probit models, and Poisson regression, score functions, Fisher information and asymptotic confidence intervals for model parameter estimates.
Michal Fabinger is the founder of the Acalonia school acalonia.com
, which aims to build an education system for a world where location does not matter. Michal’s research is in physics and economics, with the corresponding PhD training completed at Stanford and Harvard. At the University of Tokyo and the Pennsylvania State University, Michal taught courses on Deep Learning, Data Science, Statistics, Asset Pricing, International Trade, International Finance, and Development Economics.