Combining estimates in regression and classification

We consider the problem of how to combine a collection of general regression fit vectors to obtain a better predictive model. The individual fits may be from subset linear regression, ridge regression, or something more complex like a neural network. We develop a general framework for this problem and examine a cross-validation-based proposal called ''model mix'' or ''stacking'' in this context. We also derive combination methods based on the bootstrap and analytic methods and compare them in examples. Finally, we apply these ideas to classification problems where the estimated combination weights can yield insight into the structure of the problem.