Hello.  In loess regression (or gam with cubic spline smoothers, I
think) it is possible to fit models with different numbers of equivalent
parameters – thus model df –and then conduct an inferential test via
anova.  Is this a valid way of choosing the smoother df?  

Specifically, I fix a significance level of alpha and then fit a
sequence of models with increasing numbers of model df (say 2,3,4…).  I
conduct an anova to compare this sequence of models and choose the
smoother df as the one at which models fit with further increases do not
result in a significant improvement.

 

If this is not an acceptable strategy, what would people recommend
beyond using the built in cross-validation criterion?

 

Thanks for any leads.

 

Bill Shipley

Département de biologie, Université de Sherbrooke,

Sherbrooke (Québec) J1K 2R1 CANADA

Bill.Shipley@USherbrooke.ca

 <http://callisto.si.usherb.ca:8080/bshipley/>
http://callisto.si.usherb.ca:8080/bshipley/

 


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