Likelihood Inference

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Description

This lecture covers topics such as iterative weighted least squares, model checking, Poisson regression, contingency tables, mixed models, and additive models. It explains the concept of likelihood, maximum likelihood estimation, and the likelihood ratio statistic.

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Official source

Ontological neighbourhood

Statistics

Statistical inference: Statistical hypothesis testing

Data analysis: Regression analysis

Related lectures (30)

https://mediaspace.epfl.ch/media/0_kge2f1ch

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In course

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Instructor

ut do

Proident fugiat laborum amet mollit minim pariatur duis culpa ad labore minim consequat irure. Et anim culpa sunt nulla aute duis laborum consectetur labore. Ut proident cillum esse sint voluptate.

Ontological neighbourhood

Statistics

Statistical inference: Statistical hypothesis testing

Data analysis: Regression analysis

Related lectures (30)

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Linear Regression Basics

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.