Lecture

Variable Selection Methods

In course

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Description

This lecture covers various methods for variable selection in statistical modeling, including automatic selection algorithms, information criteria, and prediction error analysis. It discusses the trade-off between bias and variance, model diagnostics, and the Akaike information criterion. The instructor emphasizes the importance of selecting the right model complexity to improve prediction accuracy.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_flhpxhb7

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Ontological neighbourhood

Statistics

Data analysis: Regression analysis, Cross-validation

Statistical inference: Mathematical statistics

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