Large-scale inference

Apart from a more philosophical paragraph about the distinction between machine learning and statistical analysis in the final chapter, with the drawback of using neural nets and such as black-box methods (Page 185), there is relatively little coverage of nonparametric models in the book, the choice of “parametric formulations” (Page 96) being openly chosen. I can somehow understand this perspective for simpler settings, namely that nonparametric models offer little explanation of the production of the data. However, in more complex models, nonparametric components often are a convenient way to evacuate burdensome nuisance parameters. Again, technical aspects are not the focus of Principles of Applied Statistics, so this also explains why it does not dwell intently on nonparametric models.