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MAI0137 - Lectures

During the Lectures the theory is presented and examples intended to illustrate the theoretical results are given.
Activity When and where Description
Lecture 1TBD

Statistical models. Exponential family. Inference principles. Principles of data reduction. (LZ: 2 and 3, DB: 1.4-1.5, 3.2-3.3)

Lecture 2TBD

Estimation. Point estimation (MLE, Method of moment, Least squares, etc). Methods of evaluating estimators. (LZ: 4, DB: 1.6, 4)

Lecture 3TBD

Hypothesis testing. LRT. Interval estimation. Methods of evaluating tests and interval estimators. (LZ: 5, DB: 5)

Lecture 4TBD

Asymptotic properties of estimators and tests. (LZ: 4.3)

Lecture 5TBD

General linear models. Multiple linear regression. (LZ: 6, DB: 6)

Lecture 6TBD

ANOVA. Single-factor, two-factor and multifactor models. (LZ: 6, DB: 6)

Lecture 7TBD

Non-parametric methods.

Lecture 8TBD

Generalized linear models. Logistic regression. Poisson regression. (DB: 7 and 9)

Lecture 9TBD

Clustered and Longitudinal data. Repeated measures models. Mixed linear models. (DB: 11)

Lecture 10TBD

Introduction to Bayesian statistical inference. (DB: 12-14)

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Last updated: 2021-08-13