I wonder is it possible to construct a generalized linear modelin in that way,
- First, I will exclude the intercept term, which is standard for GAMs.
- Second, I will predict my interested dependent variable(let say y1) with a parametric model using my dependent variable let say x.
- Third, after eliminating the effect of parametric model estimates from the dependet variable, I will have a new dependent variable y2
- Then, I will predict y2 by using x by a nonparametric method.
My aim is trying to capture the effect of x on y first by a parametric model. Then, capturing the remanining effects of x on y by a non parametric method, which are not captured by parametric model.
Is this a valid method?
I will be very glad for any help. Thanks a lot.
calculate f(y1,x) where f is a parametric model(y1 is dep. and x is indep. variable)
calculate y_pred=g(y2,x) where g is a nonparametric procedure(y2 is dep. and x is indep. variable)