Maximum Likelihood Estimation

Related lectures (30)

Explores the theory and applications of likelihood ratio tests in statistical hypothesis testing.

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Explores regular exponential family models, unifying distributions like Poisson, binomial, and normal under a common framework.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Covers t-tests, p-values calculation, and comparison of coefficients.

Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.

Explores completeness, minimal sufficiency, and special statistical models, focusing on exponential and transformation families.

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