Paper "Comments on: Inference and computation with Generalized Additive Models and their extensions" by Greven and Scheipl appeared in TEST
The paper "Comments on: Inference and computation with Generalized Additive Models and their extensions (by Simon Wood)" by Sonja Greven and Fabian Scheipl appeared in TEST.
Simon Wood describes a very general framework for additive regression modeling. We wholeheartedly would like to congratulate him not only on this well-written overview but also on the work that it summarizes, much of it his own. In particular, this includes the methodological and theoretical developments, but also the availability of an implementation of much of what is described in the R package mgcv (Wood 2019). This allows these versatile modeling tools to be the basis for a whole ecosystem of follow-up work by other researchers. It also ensures that the methods are not only used by statisticians, but are truly useful for researchers with all kinds of applications ranging from ecology (Pedersen et al. 2018) to linguistics (Winter and Wieling 2016; Baayen et al. 2018).
The model class that Wood describes in Section 3.3, as it is based on the general concept of penalized regression, is even larger than might be apparent from the many examples given. Together with the comprehensive and extendable implementation, this means that many further models can be fitted. In the following sections, we describe two such extensions from our own work, which rely on the inferential techniques presented here: regression with functional data in Sect. 2 and time-to-event models in Sect. 3. We close with some comments on statistical inference and thoughts on potential extensions from our own perspective in Sects. 4 and 5.