Model selection and parameter estimation using the iterative smoothing
  method
  We compute the distribution of likelihoods from the non-parametric iterative
smoothing method over a set of mock Pantheon-like type Ia supernova datasets.
We use this likelihood distribution to test whether typical dark energy models
are consistent with the data and to perform parameter estimation. In this
approach, the consistency of a model and the data is determined without the
need for comparison with another alternative model. Simulating future
WFIRST-like data, we study type II errors and show how confidently we can
distinguish different dark energy models using this non-parametric approach.
