An approximate measure of parsimony of the Poisson-Gama Additive Models can be achieved by the expression
$$AIC=\left(D\left(y;\hat\mu\right)+2gle\right)/\left(n-\tau\right)$$
where \(gle\) is the number of degrees of freedom of the fitted model and \(\tau\) is the index of the first non-zero observation.
References
Harvey, A. C., Fernandes, C. (1989) Time series models for count data or qualitative observations. Journal of Business and Economic Statistics, 7(4):407--417
Junger, W. L. (2004) Semiparametric Poisson-Gamma models: a roughness penalty approach. MSc Dissertation. Rio de Janeiro, PUC-Rio, Department of Electrical Engineering.
Hastie, T. J., Tibshirani, R. J.(1990) Generalized Additive Models. Chapman and Hall, London
library(pgam)
data(aihrio)
attach(aihrio)
form <- ITRESP5~f(WEEK)+HOLIDAYS+rain+PM+g(tmpmax,7)+g(wet,3)
m <- pgam(form,aihrio,omega=.8,beta=.01,maxit=1e2,eps=1e-4,optim.method="BFGS")
AIC(m)