Exponential Family

Related lectures (30)

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Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

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

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Covers the basics of probability theory, random variables, joint probability, and inference.

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

Covers logistic regression and maximum likelihood estimation in depth.