Maximum Likelihood Estimation
Dive deep into the world of Maximum Likelihood Estimation (MLE) with this comprehensive 3.5-hour tutorial. Explore a wide range of probability distributions, including Bernoulli, Binomial, Continuous Uniform, Normal, Multivariate Normal, Wishart, Gumbel, Gamma, Negative Binomial, Beta, Multinomial, Double Exponential, Inverse Gamma, Lindley, and Beta Binomial. Learn essential mathematical techniques such as matrix derivatives, quadratic forms, and trace operations. Discover the Score Function and its asymptotic normality, as well as the Kaplan Meier Estimator. Gain practical experience using R for data generation, MLE calculations, and method of moments estimations. Master the theoretical foundations and practical applications of MLE across various statistical distributions, enhancing your skills in statistical inference and data analysis.