  Classical tests of goodness-of-fit aim to validate the conformity of a
postulated model to the data under study. Given their inferential nature, they
can be considered a crucial step in confirmatory data analysis. In their
standard formulation, however, they do not allow exploring how the hypothesized
model deviates from the truth nor do they provide any insight into how the
rejected model could be improved to better fit the data. The main goal of this
work is to establish a comprehensive framework for goodness-of-fit which
naturally integrates modeling, estimation, inference, and graphics. Modeling
and estimation focus on a novel formulation of smooth tests that easily extends
to arbitrary distributions, either continuous or discrete. Inference and
adequate post-selection adjustments are performed via a specially designed
smoothed bootstrap and the results are summarized via an exhaustive graphical
tool called CD-plot.
