Model selection and constraints from Holographic dark energy scenarios
  In this study we combine the expansion and the growth data in order to
investigate the ability of the three most popular holographic dark energy
models, namely event future horizon, Ricci scale and Granda-Oliveros IR
cutoffs, to fit the data. Using a standard $\chi^2$ minimization method we
place tight constraints on the free parameters of the models. Based on the
values of the Akaike and Bayesian information criteria we find that two out of
three holographic dark energy models are disfavored by the data, because they
predict a non-negligible amount of dark energy density at early enough times.
Although the growth rate data are relatively consistent with the holographic
dark energy models which are based on Ricci scale and Granda-Oliveros IR
cutoffs, the combined analysis provides strong indications against these
models. Finally, we find that the model for which the holographic dark energy
is related with the future horizon is consistent with the combined
observational data.
