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MATH-341: Linear models

Summary

Regression modelling is a fundamental tool of statistics, because it describes how the law of a random variable of interest may depend on other variables. This course aims to familiarize students with linear models and some of their extensions, which lie at the basis of more general regression model

Lectures

Instructor

Victor Panaretos

Official source

https://edu.epfl.ch/coursebook/en/linear-models-MATH-341

Moodle Page

http://go.epfl.ch/MATH-341

About this result

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Summary

Lectures

Instructor

Victor Panaretos

Official source

https://edu.epfl.ch/coursebook/en/linear-models-MATH-341

Moodle Page

http://go.epfl.ch/MATH-341

About this result

Ontological neighbourhood

Statistics

Data analysis: Regression analysis, Cross-validation

Statistical inference: Mathematical statistics

Mathematics

Algebra: Linear algebra

Lectures in this course (18)

Linear Models: Introduction

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Subspaces, Spectra, and Projections

Explores subspaces, spectra, and projections in linear algebra, including symmetric matrices and orthogonal projections.

Nested Model Selection

Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.

Model Selection: Non-Nested Model Selection

Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.

Multicollinearity: Dangers and Remedies

Explores the dangers of multicollinearity in linear models and discusses diagnostic methods and remedies.

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