On 16 Jun 2003 14:16:42 -0700, [EMAIL PROTECTED] (dave martin) wrote: [ snip, most] > > On the other hand, many many researchers use the F-test to see if > adding one more parameter to a model is beneficial. In my experience > they usually (I've seen no counterexample) use the same data to > generate the two mean square errors.
As we have said several times, "Adding one more parameter", if that is what you are doing, gives a nested model, where the F-test is proper and well-known (given: other assumptions). It is the situation that is not-nested that does not have a test, so it lends itself to the AIC or BIC -- those are attempts at borrowing the logic of the (somewhat-similar) nested tests. I think that the AIC and BIC differ mainly in how much they penalize extra degrees of freedom. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html "Taxes are the price we pay for civilization." Justice Holmes. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
