Non-parametric modeling of the cosmological data, base on the $\chi^2$
  distribution
  In the $\Lambda$CDM model, cosmological observations from the late and recent
universe reveal a puzzling $\sim 4.5\sigma$ tension in the current rate of
universe expansion. In addition to the various scenarios suggested to resolve
the tension, non-parametric modeling may provide useful insights. In this
paper, we look at three well-known non-parametric methods, the smoothing
method, the genetic algorithm, and the Gaussian process. Considering these
three methods, we employ the recent Hubble parameters data to reconstruct the
rate of universe expansion and supernovae Pantheon sample to reconstruct the
luminosity distance. In contrast to the similar studies in the literature, the
chi-squared distribution has been used to construct a reliable criterion to
select a reconstruction. Finally, we compute the current rate of universe
expansion ($H_0$) for each method, provide some discussions regarding the
performance of each approach, and compare the results.
