Write our solutions to the following problems and submit them to the course canvas by the due date to recleve credit for vour work. kecall that our standard least squares estimator of p in linear regression is given by 8 = min IY - XBII However, In ridge regression, our problem becomes 3= min |Y -X3|12 + A|13|121 (2) a) Use the identities faT r = a and aT Ar = (A + AT) a for vectors a and a and matrix A to find the solution to the least squares problem (problem 1). 1 = a and aT Ar = (A+ AT) a for vectors a and a and matrix A to find the solution to the ridge regression problem (problem 2) Recall that an additive model has the form Y = XB+ where E; ~ V(O.g-) and [b1 (X1) b2 (X1) b1(X2) ba(X2) be (X1)] by (X2) (bI (Xn) b2 (Xn) ba (Kn)) for some prespecified basis functions {b,.. , b}. The solution to the additive model problem is still 8 = min |Y - X3|12 It can be shown that. to penalize wigglv, overfitting behavior, we solve the following problem 3= min||Y - X3|12 + ABT