Ridge regression is a means to accommodate (some of the consequences of) collinearity but has found a much wider use. This tutorial introduces ridge regression and reviews its most important properties in statistically layman terms.
In regression analysis collinearity (or confounding) refers to the phenomenon of strong linear association between explanatory variables. Especially in the current data age, where the number of explanatory variables often exceeds the sample size, collinearity is a pressing problem. Ridge regression is a means to accommodate (some of the consequences of) collinearity but has found a much wider use. This tutorial introduces ridge regression and reviews its most important properties in statistically layman terms, although familiarity with regression analysis is assumed. It then shows applications of ridge regression in the biomedical context, with examples stemming from among others cancer research, epidemiology, and movement sciences.
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| Location: | VU, MF building, A415 |
| Costs: | Free |
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APH Methodology |
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| Contact | Anouk Weverling | aph.methodology@amsterdamumc.nl |