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The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family of models, while controlling the impact of statistical fluctuations.
Learning multivariate new physics ,
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer and M. Zanetti, · 1912
Earlier work this paper cites.
On the Problem of the Most Efficient Tests of Statistical Hypotheses ,
J. Neyman and E. S. Pearson, · 1933
Earlier work this paper cites.
Clarification of the Use of Chi Square and Likelihood Functions in Fits to Histograms ,
S. Baker and R. D. Cousins, · 1984
Earlier work this paper cites.
On multivariate goodness of fit and two sample testing ,
J. H. Friedman, · 2003
Earlier work this paper cites.
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G. Claeskens and N. L. Hjort, · 2004
Earlier work this paper cites.
Model-Independent Jets plus Missing Energy Searches ,
J. Alwall, M.-P. Le, M. Lisanti and J. G. Wacker, · 2009
Earlier work this paper cites.
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M. Williams, · 2010
Earlier work this paper cites.
John Wiley & Sons, Ltd,
Goodness of Fit , chap. 3, pp. 39–61, · 2013
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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