Open Access
Issue
EPJ Web of Conferences
Volume 55, 2013
SOS 2012 – IN2P3 School of Statistics
Article Number 03003
Number of page(s) 29
Section Application to Data Analyses
DOI https://doi.org/10.1051/epjconf/20135403003
Published online 01 July 2013
  1. J. Neyman, E. Pearson, “On the Problem of the Most Efficient Tests of Statistical Hypotheses”, Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 231 (1933) 289. [Google Scholar]
  2. S. Wilks, “The large-sample distribution of the likelihood ratio for testing composite hypotheses”, Ann. Math. Stat., 9 (1938) 60. [Google Scholar]
  3. S. Baker, R.D. Cousins, “Clarification of the use of chi-square and likelihood functions in fit to histograms”, Nucl. Instr. Meth., A221 (1984), 437. [Google Scholar]
  4. G. Cowan, K. Cranmer, E. Gross and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”, Eur.Phys.J. C71 (2011) 1554. [Google Scholar]
  5. O. Helene, “Upper limit of peak area”, Nucl. Instr. and Meth. A212 (1983) 319. [Google Scholar]
  6. G. D’Agostini, “Bayesian Reasoning in Data Analysis: A Critical Introduction”, World Scientific (2003) ISBN 981-238-356-5, [Google Scholar]
  7. J. Jeffreys, “An Invariant Form for the Prior Probability in Estimation Problems”, Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences 186 no. 1007 ((1946) 453. [Google Scholar]
  8. G. Zech, “Upper limits in experiments with background or measurement errors”, Nucl. Instr. and Meth. A277 (1989) 608. [Google Scholar]
  9. V.L. Highland, R.D. Cousins, “Comment on “Upper limits in experiments with background or measurement errors” [Nucl. Instr. and Meth. A 277 (1989) 608–610]”, [Google Scholar]
  10. Nucl. Instr. and Meth. A398 (1989), 429. [Google Scholar]
  11. G. Zech, “Reply to “Comment on “Upper limits in experiments with background or measurement errors” [Nucl. Instr. and Meth. A 277 (1989) 608-610]” ”, [Google Scholar]
  12. Nucl. Instr. and Meth. A398 (1989) 431. [Google Scholar]
  13. J.Neyman, J, “Outline of a theory of statistical estimation based on the clasiscal theory of probability”, Philosophical Transactions of the Royal Society of London, A236, no. 767 (1937), 333. [Google Scholar]
  14. G.J. Feldman, R.D. Cousins, “Unified approach to the classical statistical analysis of small signals”, Phys. Rev. D57 (1998) 3873. [Google Scholar]
  15. C. Amsler, C. it et al. (Particle Data Group), “The Review of Particle Physics”, Phys. Lett. B667 (2008) 1. [Google Scholar]
  16. G. Abbiendi et al. (The LEP Working Group for Higgs Boson Searches), “Search for the Standard Model Higgs Boson at LEP”, Phys. Lett. B565 (2003) 61. [Google Scholar]
  17. A.L. Read, “Modified frequentist analysis of search results (the CLs method)”, 1st Workshop on Confidence Limits", CERN (2000). [Google Scholar]
  18. B.A. Berg, “Markov Chain Monte Carlo Simulations and Their Statistical Analysis”, World Scientific", Singapore (2004). [Google Scholar]
  19. R.D. Cousins, V.L. Highland, “Incorporating Syst ematic Uncertainties into an Upper Limit”, Nucl. Instr. Meth. A320 (1992) 331. [Google Scholar]
  20. L. Lista, “Including gaussian uncertainty on the background estimate for upper limit calculations using Poissonian sampling”, Nucl. Instr. Meth. A517 (2004) 360. [Google Scholar]
  21. G. Cowan et al., “Asymptotic formulae for likelihood-based tests of new physics” EPJC 71 (2011) 1554. [Google Scholar]
  22. The ATLAS Collaboration, the CMS collaboration, the LHC Higgs combination group, “Procedure for the LHC Higgs boson search combination in Summer 2011”, ATL-PHYS-PUB-2011-IN2P3 School Of Statistics, Autrans 011, CMS NOTE-2011-005 (2011) [Google Scholar]
  23. I. Asimov, “Franchise”, in I. Asimov, “The Complete Stories”, vol. 1, Broadway Books, New York, 1990. [Google Scholar]
  24. E. Gross, O. Vitells, “Trial factors for the look elsewhere effect in high energy physics”, Eur. Phys. J. C70 (2010) 525. [Google Scholar]
  25. R.B. Davies, “Hypothesistestingwhenanuisance parameter is present only under the alternative”, Biometrika 74 (1987), 33. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.