Hello, I�m using ridge regression and I have a problem finding the variance of the constant coefficient. So, I have the model y=b_0+b_1*x_1+..+b_k*x_k+e and I "standardize" it in order to use ridge regression y_(st)=beta_1*x_(st),1+..+beta_k*x_(st),k+e_(st) The variance of the beta�s can be found from s^2*(R+r*I)^(-2)*R, where s^2 the estimator of e_(st)^2, R the correlation matrix (which is equal to (X_(st))^T *X_(st) ) and r the value I use for the ridge regression. Now I can transform back and calculate the variance of b_i, i=1,..,k from var[b_i]=var[beta_i]*(s_y)^2/(s_i)^2, with (s_y)^2=(sum(y_j)^2)-n*mean(y)^2 and (s_i)^2 analog for x_i. But how do I find the variance for b_0??
Thanks and sorry for my bad english, Lydia . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
