690
L. Lyons
49. S. Whiteson and D. Whiteson, “Stochastic Optimization for Collision Selection in High
Energy Physics.” IAAI 2007: Proceedings of the Nineteenth Annual Innovative Applications
of Artificial Intelligence Conference (July 2007) 1819.
50. N. Tishby and N. Zaslavsky, “Deep Learning and the Information Bottleneck Principle”
(2015), https://arxiv.org/abs/1503.02406;
R. Shwartz-Ziv and N. Tishby, “Opening the Black Box of Deep Neural Networks via
Information” (2017), https://arxiv.org/abs/1703.00810
51. L. Lyons, “Raster scan or 2-D approach?”, https://arxiv.org/pdf/1404.7395.pdf
52. A. Michelson and E.Morley, “On the Relative Motion of the Earth and the Luminiferous
Ether”, American Journal of Science. 34 (1887) 203: 333.
53. I. Narsky, “Comparison of upper limits”, in ref. [2].
54. G. J. Feldman and R. D. Cousins, Phys. Rev. D57 (1998) 3873.
55. D. R. Cox, “Some problems connected with statistical inference”, Annals of Mathematical
Statistics 29 (1958) 357.
56. B. Sen, M. Walker and M. Woodroofe, “On the Unified Method with Nuisance Parameters”,
Statistica Sinica 19 (2009) 301
57. G. Punzi, “Sensitivity of searches for new signals and its optimisation”, ref. [4], p. 235.
58. A. L. Read, “Modified frequentist analysis of search results”, ref. [1], p. 81; “Presentation of
search results—the CL s method”, ref. [3], p. 11.
T. Junk, “Confidence level computation for combining searches with small statistics”, NIM A
434 (1999) 435.
59. W. A. Rolke, A. M. Lopez and J. Conrad, Nuclear Instruments and Methods A551 (2005)
493.
60. J. Heinrich et al. “Interval estimation in the presence of nuisance parameters. 1.
Bayesian approach”, CDF note 7117 (2004), https://www-cdf.fnal.gov/physics/statistics/
notes/cdf7117_bayesianlimit.pdf
61. L. Demortier, “A fully Bayesian computation of upper limits for Poisson processes”, CDF
note 5928 (2004).
62. J. Heinrich, “The Bayesian approach to setting limits: what to avoid”, ref. [5], p 98.
63. G. Punzi, “Ordering algorithms and confidence intervals in the presence of nuisance
parameters”, ref. [5], p. 88.
64. R. Cousins, Nuclear Instruments and Methods A417 (1998) 391.
65. R. D. Cousins and V. L. Highland, Nuclear Instruments and Methods A320 (1992) 331.
66. J. Heinrich, “Review of Banff challenge on upper limits”, ref. [7], p. 125.
67. CDF Statistics Committee, “Recommendations concerning limits” (2005), http://www-cdf.
fnal.gov/physics/statistics/recommendations/limits.txt.
68. A.Kolmogorov, “Sulla determinazione empirica di una legge di distribuzione”, G. Ist. Ital.
Attuari, 4 (1933) 83.
N. Smirnov, “Table for estimating the goodness of fit of empirical distributions”, Annals of
Mathematical Statistics. 19 (2) (1948) 279, doi:10.1214/aoms/1177730256
69. T. W. Anderson and D. A. Darling, “Asymptotic theory of certain ‘goodness-of-fit’ criteria based on stochastic processes”, Annals of Mathematical Statistics. 23 (1952) 193,
doi:10.1214/aoms/1177729437; and “A Test of Goodness-of-Fit”, Journal of the American
Statistical Association, 49 (1954) 765, doi:10.2307/2281537
70. L. Lyons, D. Gibaut and P. Clifford, Nuclear Instr. Meth. 270 (1988) 210.
71. L. Lyons, A. Martin and D. Saxon, Phys Rev D41 (1990) 982.
72. T. Trippe and Particle Data Group, private communication.
73. N. Suzuki et al, “Hubble Space Telescope cluster supernova study. V: Improving the
Dark Energy constraints above z > 1 and building an early-type-hosted supernova sample”,Astrophys J 746 (2012) 85, arXiv:1105.3470[astro-ph.CO]
74. L. Lyons and N. Wardle, “Statistical issues in searches for new phenomena in High Energy
Physics”, J Phys G Nucl Part Phys 48 (2018) 033001
L. Lyons
49. S. Whiteson and D. Whiteson, “Stochastic Optimization for Collision Selection in High
Energy Physics.” IAAI 2007: Proceedings of the Nineteenth Annual Innovative Applications
of Artificial Intelligence Conference (July 2007) 1819.
50. N. Tishby and N. Zaslavsky, “Deep Learning and the Information Bottleneck Principle”
(2015), https://arxiv.org/abs/1503.02406;
R. Shwartz-Ziv and N. Tishby, “Opening the Black Box of Deep Neural Networks via
Information” (2017), https://arxiv.org/abs/1703.00810
51. L. Lyons, “Raster scan or 2-D approach?”, https://arxiv.org/pdf/1404.7395.pdf
52. A. Michelson and E.Morley, “On the Relative Motion of the Earth and the Luminiferous
Ether”, American Journal of Science. 34 (1887) 203: 333.
53. I. Narsky, “Comparison of upper limits”, in ref. [2].
54. G. J. Feldman and R. D. Cousins, Phys. Rev. D57 (1998) 3873.
55. D. R. Cox, “Some problems connected with statistical inference”, Annals of Mathematical
Statistics 29 (1958) 357.
56. B. Sen, M. Walker and M. Woodroofe, “On the Unified Method with Nuisance Parameters”,
Statistica Sinica 19 (2009) 301
57. G. Punzi, “Sensitivity of searches for new signals and its optimisation”, ref. [4], p. 235.
58. A. L. Read, “Modified frequentist analysis of search results”, ref. [1], p. 81; “Presentation of
search results—the CL s method”, ref. [3], p. 11.
T. Junk, “Confidence level computation for combining searches with small statistics”, NIM A
434 (1999) 435.
59. W. A. Rolke, A. M. Lopez and J. Conrad, Nuclear Instruments and Methods A551 (2005)
493.
60. J. Heinrich et al. “Interval estimation in the presence of nuisance parameters. 1.
Bayesian approach”, CDF note 7117 (2004), https://www-cdf.fnal.gov/physics/statistics/
notes/cdf7117_bayesianlimit.pdf
61. L. Demortier, “A fully Bayesian computation of upper limits for Poisson processes”, CDF
note 5928 (2004).
62. J. Heinrich, “The Bayesian approach to setting limits: what to avoid”, ref. [5], p 98.
63. G. Punzi, “Ordering algorithms and confidence intervals in the presence of nuisance
parameters”, ref. [5], p. 88.
64. R. Cousins, Nuclear Instruments and Methods A417 (1998) 391.
65. R. D. Cousins and V. L. Highland, Nuclear Instruments and Methods A320 (1992) 331.
66. J. Heinrich, “Review of Banff challenge on upper limits”, ref. [7], p. 125.
67. CDF Statistics Committee, “Recommendations concerning limits” (2005), http://www-cdf.
fnal.gov/physics/statistics/recommendations/limits.txt.
68. A.Kolmogorov, “Sulla determinazione empirica di una legge di distribuzione”, G. Ist. Ital.
Attuari, 4 (1933) 83.
N. Smirnov, “Table for estimating the goodness of fit of empirical distributions”, Annals of
Mathematical Statistics. 19 (2) (1948) 279, doi:10.1214/aoms/1177730256
69. T. W. Anderson and D. A. Darling, “Asymptotic theory of certain ‘goodness-of-fit’ criteria based on stochastic processes”, Annals of Mathematical Statistics. 23 (1952) 193,
doi:10.1214/aoms/1177729437; and “A Test of Goodness-of-Fit”, Journal of the American
Statistical Association, 49 (1954) 765, doi:10.2307/2281537
70. L. Lyons, D. Gibaut and P. Clifford, Nuclear Instr. Meth. 270 (1988) 210.
71. L. Lyons, A. Martin and D. Saxon, Phys Rev D41 (1990) 982.
72. T. Trippe and Particle Data Group, private communication.
73. N. Suzuki et al, “Hubble Space Telescope cluster supernova study. V: Improving the
Dark Energy constraints above z > 1 and building an early-type-hosted supernova sample”,Astrophys J 746 (2012) 85, arXiv:1105.3470[astro-ph.CO]
74. L. Lyons and N. Wardle, “Statistical issues in searches for new phenomena in High Energy
Physics”, J Phys G Nucl Part Phys 48 (2018) 033001