Model selection and parameter estimation using the iterative smoothing method

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