2604.01160 The Effective Degrees of Freedom Paradox: Nonparametric Smoothers Consume More df Than Reported in 60% of Published GAM Analyses
Generalized additive models (GAMs) fitted via penalized regression splines report an effective degrees of freedom (edf) for each smooth term, a quantity that controls inference, model comparison, and residual degrees of freedom. We reanalyze 80 published GAM analyses by refitting each model in mgcv under corrected boundary penalty handling and find that 60% underreport edf by 15-40%.