When doing linear regression, if we include a variable that is severely correlated with our regressors, we will be inflating our standard errors for those correlated variables. This happens because, if two variables are correlated, the model can't be sure to which one it should be assigning the effect/coefficient. Ridge Regression allows us to model highly correlated regressors, by introducing a bias. Our first thought in statistics is to avoid biased coefficients at all cost. But they might not be that bad after all: if the coefficients are biased but have a much smaller variance than our baseline method, we will be in a better situation. Unbiased coefficients with a high variance will change a lot between different model runs (unstable) but they will converge in probability to the right place. Biased coefficients with a low variance will be quite stable...
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You're reading from R Statistics Cookbook
Product typeBook
Published inMar 2019
Reading LevelExpert
PublisherPackt
ISBN-139781789802566
Edition1st Edition
Languages
Tools
Concepts
Author (1)
Francisco Juretig
Francisco Juretig
Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.
Read more about Francisco Juretig
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R Statistics CookbookPublished in: Mar 2019Publisher: PacktISBN-13: 9781789802566
© 2019 Packt Publishing Limited All Rights Reserved
Author (1)
Francisco Juretig
Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.
Read more about Francisco Juretig