What are the leverage values for Ridge regression?
Posted by José Bayoán Santiago Calderón, at stats.stackexchange.com,
In linear least squares the parameter estimates are: $\hat{\beta} = \left(X^{\top}X\right)^{-1}X^{\top}y$. In Ridge regression…
In linear least squares the parameter estimates are: $\hat{\beta} = \left(X^{\top}X\right)^{-1}X^{\top}y$. In Ridge regression…
In ordinary least squares, for example in an experimental design case, I obtain the regression coefficents by: $ \hat B = {({X^t…
Doing ridge regression in R I have…
On what basis might one accept/reject a hypothesis when running ridge regression? For example, if I have five predictor…