[R] Nonnormal Residuals and GAMs

[Collin Lynch]

Greetings, My question is more algorithmic than prectical.  What I am
trying to determine is, are the GAM algorithms used in the mgcv package
affected by nonnormally-distributed residuals?

As I understand the theory of linear models the Gauss-Markov theorem
guarantees that least-squares regression is optimal over all unbiased
estimators iff the data meet the conditions linearity, homoscedasticity,
independence, and normally-distributed residuals.  Absent the last
requirement it is optimal but only over unbiased linear estimators.

What I am trying to determine is whether or not it is necessary to check
for normally-distributed errors in a GAM from mgcv.  I know that the
unsmoothed terms, if any, will be fitted by ordinary least-squares but I
am unsure whether the default Penalized Iteratively Reweighted Least
Squares method used in the package is also based upon this assumption or
falls under any analogue to the Gauss-Markov Theorem.

Thank you in advance for any help.

	Sincrely,
	Collin Lynch.