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Keywords:

  • Akaike's information criterion;
  • Generalized additive mixed models;
  • Generalized additive models;
  • Generalized approximate cross-validation;
  • Generalized cross-validation;
  • Penalized likelihood;
  • Penalized regression splines;
  • Stable computation

Summary.  Existing computationally efficient methods for penalized likelihood generalized additive model fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing ‘whole model’ criteria these problems disappear, but until now attempts to do this have employed finite-difference-based optimization schemes which are computationally inefficient and can suffer from false convergence. The paper develops the first computationally efficient method for direct generalized additive model smoothness selection. It is highly stable, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes that are based on working model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models.