Quantitude S5E09 Regularized Variable Selection Methods
Nov 28, 2023
In this podcast, the hosts discuss regularization methods such as ridge, LASSO, and elastic net procedures for variable selection. They also touch on topics like bowdlerizing, disturbance in the force, and letting go of truth. They explore the concept of regularization, its applications in statistics, and the tension between explanation and prediction in model selection. The advantages of regularization methods, such as enhanced replicability and handling collinearity, are highlighted.
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7
Introduction
00:00 • 2min
Discussing the Meaning of 'Boldization' and 'Bowlerize'
02:03 • 2min
Regularized Variable Selection Methods in Regression Analysis
04:09 • 16min
Regularization in Variable Selection Methods
19:57 • 17min
Tension Between Explanation and Prediction in Model Selection
37:03 • 4min
Regularized Variable Selection Methods: Simplifying Variables and Handling Collinearity
41:18 • 7min
Moving from Prediction to Explanation: A Horse Race in Regression Models
47:48 • 4min