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Statistical Issues
A new version of the C statistic
It is well-known (I hope) to all users of XSPEC that the
chi-squared statistic is not appropriate if there are small
numbers of counts (< 25) in any bins in the spectrum.
The solution is to use the C statistic (Cash 1978 ApJ 228, 939)
which assumes only that the counts in each bin follow the
Poisson distribution. One drawback of this statistic is that
any background must be explicitly modelled.
Building on the work of Wachter et al. (1979 ApJ 230, 274) we have
derived and implemented a new statistic which works on data
when there is a background spectrum. The basic trick is to
use a background model with one parameter for each spectral bin
and to solve analytically for these parameter values.
With this new statistic it is no longer necessary to use the
dangerous technique of grouping together spectral bins till the
combined counts are in the Gaussian regime and chi-squared can be
used.
Bayesian methods
A completely different approach to the same problem is provided
by Bayesian analysis. A straightforward extension of the work of
Loredo can be used to derive the probability distribution of the
model parameters of interest.
The equivalent in Bayesian terms of confidence contours is a
joint probability distribution. This corresponds to what most
astronomers think they mean when they talk of confidence
contours but requires rather more CPU to calculate.
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