Smoothing Parameter and Model Selection for General Smooth Models

This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods
\nconstructed in terms of unknown smooth functions of covariates. Gaussian random effects and
\nparametric terms may also be present. By construction the method is numerically stable and convergent,
\nand enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known
\nproblem with AIC for such models, thereby improving the range of model selection tools available. The
\nsmooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties
\nmeasuring function smoothness. Model estimation is by…

• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/I000917/1, EP/K005251/2, EP/K005251/1