Speakers
Dr
Alberto Ribon
(CERN)Dr
Andreas Pfeiffer
(CERN)Dr
Barbara Mascialino
(INFN Genova)Dr
Maria Grazia Pia
(INFN GENOVA)Dr
Paolo Viarengo
(IST Genova)
Description
Many Goodness-of-Fit tests have been collected in a new open-source Statistical
Toolkit: Chi-squared, Kolmogorov-Smirnov, Goodman, Kuiper, Cramer-von Mises,
Anderson-Darling, Tiku, Watson, as well as novel weighted formulations of some tests.
None of the Goodness-of-Fit tests included in the toolkit is optimal for any analysis
case. Statistics does not provide a universal recipe to identify the most appropriate
test to compare two distributions; the limited available guidelines derive from
relative power comparisons of samples drawn from smooth theoretical distributions.
A comprehensive study has been performed to provide general guidelines for the
practical choice of the most suitable Goodness-of-Fit test under general
non-parametric conditions. Quantitative comparisons among the two-sample
Goodness-of-Fit tests contained in the Goodness-of-Fit Statistical Toolkit are presented.
This study is the most complete and general approach so far available to characterize
the power of goodness-of-fit tests for the comparison of two data distributions; it
provides guidance to the user to identify the most appropriate test for his/her
analysis on an objective basis.
Authors
Dr
Alberto Ribon
(CERN)
Dr
Andreas Pfeiffer
(CERN)
Dr
Barbara Mascialino
(INFN Genova)
Dr
Maria Grazia Pia
(INFN GENOVA)
Dr
Paolo Viarengo
(IST Genova)
