Analysis of recent type Ia supernova data based on evolving dark energy
  models
  We study characters of recent type Ia supernova (SNIa) data using evolving
dark energy models with changing equation of state parameter w. We consider
sudden-jump approximation of w for some chosen redshift spans with double
transitions, and constrain these models based on Markov Chain Monte Carlo
(MCMC) method using the SNIa data (Constitution, Union, Union2) together with
baryon acoustic oscillation A parameter and cosmic microwave background shift
parameter in a flat background. In the double-transition model the Constitution
data shows deviation outside 1 sigma from LCDM model at low (z < 0.2) and
middle (0.2 < z < 0.4) redshift bins whereas no such deviations are noticeable
in the Union and Union2 data. By analyzing the Union members in the
Constitution set, however, we show that the same difference is actually due to
different calibration of the same Union sample in the Constitution set, and is
not due to new data added in the Constitution set. All detected deviations are
within 2 sigma from the LCDM world model. From the LCDM mock data analysis, we
quantify biases in the dark energy equation of state parameters induced by
insufficient data with inhomogeneous distribution of data points in the
redshift space and distance modulus errors. We demonstrate that location of
peak in the distribution of arithmetic means (computed from the MCMC chain for
each mock data) behaves as an unbiased estimator for the average bias, which is
valid even for non-symmetric likelihood distributions.
