Ridge Regression and Cross Validation in scikit

My dissertation work, Ridge Restricted Maximum Likelihood (RREML) is an extension of Ridge Regression applied to parametric covariance structures.  In my dissertation we applied it to Spatial Statistics, but it would also apply to Time Series as well.

One of the things that I struggled with in my dissertation approach was choosing the appropriate ridge constant.  I never got around to the much more favorable Cross Validation approach, but I instead used a secondary likelihood approach to estimate the ridge constant.

As I'm writing my algorithm in Python, I'm definitely going to leverage the scikit Ridge Regression approach to apply it to my RREML model.

http://scikit-learn.org/stable/modules/linear_model.html#ridge-regression