  A model-independent method to study the possible evolution of dark energy is
presented. Optimal estimates of the dark energy equation of state w are
obtained from current supernovae data from Riess et al. (2004) following a
principal components approach. We assess the impact of varying the number of
piecewise constant estimates of w using a model selection method, the Bayesian
information criterion, and compare the most favored models with some
parametrizations commonly used in the literature. Although data seem to prefer
a cosmological constant, some models are only moderately disfavored by our
selection criterion: a constant w, w linear in the scale factor, w linear in
redshift and the two-parameter models introduced here. Among these, the models
we find by optimization are slightly preferred. However, current data do not
allow us to draw a conclusion on the possible evolution of dark energy.
Interestingly, the best fits for all varying-w models exhibit a w<-1 at low
redshifts.
