[R] Multi-output regression

Hi, I have a question regarding the modeling methodology of the following problem: * I have two data sets {X_i,y_i} {X_i,z_i}, i=1..N, where y_i = f(X_i) + i.i.d. Gaussian noise and z_i = g(X_i) + i.i.d. Gaussian noise * I apply bayesian linear regression to each of them and obtain

Re: [R] Multi-output regression

I am not a Bayesian. In the non-Bayesian case you would use SUR to model both equations simultaneously. If both use the exact same matrix of data, X (i.e., the value are numerically absolutely identical), then SUR will collapse to OLS. In that sense you get a combined estimate using SUR that

[R] Multi Output Regression datasets

Dear all, My name is Pedro Latorre Carmona and I work at the Computer Languages and Systems Department of the Jaume I University in Castellon (Spain). I get in contact with you because I am currently working in the development of feature selection methods for multi-output regression