1932

Abstract

Stephen Elliott Fienberg was the founding editor of the . Steve had an outsized personality and a passion for statistical science that was quite unique, and he combined these with his legendary energy to provide a remarkable level of leadership for the statistical science community, and a sweeping vision of the importance of statistical arguments for science, health and policy. The editorial team of the is working hard to carry on his legacy for the journal. In this article we highlight some of his contributions through the voices of his students and collaborators. It is by no means a comprehensive assessment of his scholarship, but we hope it provides a window into his impact and influence on several generations of scholars. As Reid & Stigler (2017) wrote in Volume 4, “his lasting imprint on the science of statistics and its application defies simple categorization.”

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2019-03-07
2024-10-03
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Literature Cited

  1. Airoldi EM, Anderson AG, Fienberg SE, Skinner KK 2006. Who wrote Ronald Reagan's radio addresses?. Bayesian Anal 1:289–320
    [Google Scholar]
  2. Airoldi EM, Blei D, Erosheva EA, Fienberg SE 2014. Handbook of Mixed Membership Models and Their Applications Boca Raton, FL: CRC
    [Google Scholar]
  3. Airoldi EM, Blei DM, Fienberg SE, Xing EP 2008. Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9:1981–2014
    [Google Scholar]
  4. Anderson MJ. 1988. The American Census: A Social History New Haven, CT: Yale Univ. Press
    [Google Scholar]
  5. Anderson MJ, Fienberg SE 1996. An adjusted census in 1990: the Supreme Court decides. CHANCE 9:4–9
    [Google Scholar]
  6. Anderson MJ, Fienberg SE 1999. Who Counts? The Politics of Census Taking in Contemporary America New York: Russell Sage
    [Google Scholar]
  7. Anderson MJ, Fienberg SE 2000. Census 2000 controversies. CHANCE 13:22–30
    [Google Scholar]
  8. Anderson MJ, Seltzer W 2009. Federal statistical confidentiality and business data: twentieth century challenges and continuing issues. J. Priv. Confid. 1:7–52
    [Google Scholar]
  9. Bishop YM, Fienberg SE, Holland P 1975. Discrete Multivariate Analysis: Theory and Practice Cambridge, MA: MIT Press
    [Google Scholar]
  10. Blei DM. 2014. Build, compute, critique, repeat: data analysis with latent variable models. Annu. Rev. Stat. Appl. 1:203–32
    [Google Scholar]
  11. Blei DM, Lafferty JD 2006. Dynamic topic models. Proceedings of the 23rd International Conference on Machine Learning113–20 New York: ACM
    [Google Scholar]
  12. Chan S, Airoldi EM 2014. A consistent histogram estimator for exchangeable graph models. Proceedings of the 31st International Conference on International Conference on Machine Learning208–16 Brookline, MA: Microtome
    [Google Scholar]
  13. Chun AY, Schouten B, Wagner J 2017. JOS special issue on responsive and adaptive survey design: looking back to see forward—editorial. J. Off. Stat. 33:571–77
    [Google Scholar]
  14. DeGroot MH, Fienberg SE 1983. The comparison and evaluation of forecasters. Statistician 32:12–22
    [Google Scholar]
  15. DeGroot MH, Fienberg SE, Kadane JB 1983. Statistics and the Law New York: Wiley
    [Google Scholar]
  16. Dempster AP. 1969. Elements of Continuous Multivariate Analysis Boston: Addison Wesley Longman
    [Google Scholar]
  17. Devlin B, Daniels M, Roeder K 1997a. The heritability of IQ. Nature 388:468–71
    [Google Scholar]
  18. Devlin B, Fienberg SE, Resnick D, Roeder K 1995a. Galton redux: eugenics, intelligence, race, and society. J. Am. Stat. Assoc. 90:1483–88
    [Google Scholar]
  19. Devlin B, Fienberg SE, Resnick D, Roeder K 1995b. Wringing the bell curve: a cautionary tale about the relationships among race, genes and IQ. CHANCE 3:27–36
    [Google Scholar]
  20. Devlin B, Fienberg SE, Resnick DP, Roeder K 2001. Intelligence and success: Is it all in the genes?. Race and Intelligence: Separating Science from Myth JM Fish 355–68 New Jersey: Lawrence Erlbaum Assoc.
    [Google Scholar]
  21. Devlin B, Fienberg SE, Resnick D, Roeder K 1997b. Intelligence, Genes and Success: Scientists Respond to The Bell Curve New York: Springer
    [Google Scholar]
  22. Diaconis P, Sturmfels B 1998. Algebraic algorithms for sampling from conditional distributions. Ann. Stat. 26:363–97
    [Google Scholar]
  23. Dobra A, Fienberg SE, Rinaldo A, Slavkovic A, Zhou Y 2008. Algebraic statistics and contingency table problems: estimation and disclosure limitation. Emerging Applications of Algebraic Geometry M Putinar, S Sullivant 63–88 New York: Springer
    [Google Scholar]
  24. Dobra A, Fienberg SE, Trottini M 2003. Assessing the risk of disclosure of confidential categorical data. Bayesian Statistics 7, Proceedings of the Seventh Valencia International Meeting on Bayesian Statistics JM Bernardo, MJ Bayarri, AP Dawid, JO Berger, D Heckerman et al.125–44 Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  25. Dobra A, Sullivant S 2004. A divide-and-conquer algorithm for generating Markov bases of multi-way tables. Comput. Stat. 19:347–66
    [Google Scholar]
  26. Eriksson N, Fienberg SE, Rinaldo A, Sullivant S 2006. Polyhedral conditions for the nonexistence of the MLE for hierarchical log-linear models. J. Symb. Comput. 41:222–33
    [Google Scholar]
  27. Erosheva EA. 2005. Comparing latent structures of the grade of membership, Rasch, and latent class models. Psychometrika 70:619–28
    [Google Scholar]
  28. Erosheva EA, Fienberg SE 2005. Bayesian mixed membership models for soft clustering and classification. Classification—The Ubiquitous Challenge C Weihs, W Gaul 11–26 New York: Springer
    [Google Scholar]
  29. Erosheva EA, Fienberg SE, Joutard C 2007. Describing disability through individual-level mixture models for multivariate binary data. Ann. Appl. Stat. 1:502–37
    [Google Scholar]
  30. Erosheva EA, Fienberg SE, Lafferty J 2004. Mixed-membership models of scientific publications. PNAS 101:5220–27
    [Google Scholar]
  31. Fienberg SE. 1968. The geometry of an r×c contingency table. Ann. Math. Stat. 39:1186–90
    [Google Scholar]
  32. Fienberg SE. 1982. Statistical evidence of discrimination: comment. J. Am. Stat. Assoc. 77:784–87
    [Google Scholar]
  33. Fienberg SE. 2011. Bayesian models and methods in public policy and government settings. Stat. Sci. 26:212–26
    [Google Scholar]
  34. Fienberg SE 1989. The Evolving Role of Statistical Assessments as Evidence in Courts New York: Springer
    [Google Scholar]
  35. Fienberg SE, Hersh P, Rinaldo A, Zhou Y 2007. Maximum likelihood estimation in latent class models for contingency table data. Algebraic and Geometric Methods in Statistics P Gibilisco, E Riccomagno, MP Rogantin, HP Wynn 27–63 Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  36. Fienberg SE, Holland PW 1972. On the choice of flattening constants for estimating multinomial probabilities. J. Multivar. Anal. 2:127–34
    [Google Scholar]
  37. Fienberg SE, Holland PW 1973. Simultaneous estimation of multinomial cell probabilities. J. Am. Stat. Assoc. 68:683–91
    [Google Scholar]
  38. Fienberg SE, Johnson MS, Junker BW 1999. Classical multi-level and Bayesian approaches to population size estimation using data from multiple lists. J. R. Stat. Soc. A 162:383–405
    [Google Scholar]
  39. Fienberg SE, Kadane JB 1983. The presentation of Bayesian statistical analyses in legal proceedings. J. R. Stat. Soc. D 32:88–98
    [Google Scholar]
  40. Fienberg SE, Lee SK 1975. On small world statistics. Psychometrika 40:2219–28
    [Google Scholar]
  41. Fienberg SE, Meyer MM, Wasserman SS 1985. Statistical analysis of multiple sociometric relations. J. Am. Stat. Assoc. 80:38951–67
    [Google Scholar]
  42. Fienberg SE, Petrović S, Rinaldo A 2011. Algebraic statistics for p1 random graph models: Markov bases and their uses. Looking Back: Proceedings of a Conference in Honor of Paul W. Holland NJ Dorans, S Sinharay 21–38 New York: Springer
    [Google Scholar]
  43. Fienberg SE, Rinaldo A 2007. Three centuries of categorical data analysis: log-linear models and maximum likelihood estimation. J. Stat. Plan. Inference 137:3420–45
    [Google Scholar]
  44. Fienberg SE, Rinaldo A 2012. Maximum likelihood estimation in log-linear models. Ann. Stat. 40:996–1023
    [Google Scholar]
  45. Fienberg SE, Rinaldo A, Yang X 2010. Differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables. International Conference on Privacy in Statistical Databases J Domingo-Ferrer, E Magkos 187–99 New York: Springer
    [Google Scholar]
  46. Fienberg SE, Schervish MJ 1986. The relevance of Bayesian inference for the presentation of statistical evidence and for legal decision making. Boston Univ. Law Rev. 66:771
    [Google Scholar]
  47. Fienberg SE, Shmueli G 2005. Statistical issues and challenges associated with rapid detection of bio-terrorist attacks. Stat. Med. 24:513–29
    [Google Scholar]
  48. Fienberg SE, Shmueli G 2006. Comment on “A Bayesian dynamic model for influenza surveillance”, by Sebastiani, Mandl, Szolovits, Kohane, and Ramoni. Stat. Med. 25:1821–22
    [Google Scholar]
  49. Fienberg SE, Straf M 1982. Statistical assessments as evidence. J. R. Stat. Soc. A 145:410–21
    [Google Scholar]
  50. Fienberg SE, Wasserman SS 1981. Categorical data analysis of single sociometric relations. Sociol. Methodol. 12:156–92
    [Google Scholar]
  51. Fienberg SE, Willenborg LCRJ 1998. Introduction to the special issue: disclosure limitation methods for protecting the confidentiality of statistical data. J. Off. Stat. 14:337–45
    [Google Scholar]
  52. Finkelstein MO, Fairley WB 1970. A Bayesian approach to identification evidence. Harv. Law Rev. 83:489–517
    [Google Scholar]
  53. Finkelstein MO, Fairley WB 1971. The continuing debate over mathematics in the law of evidence: a comment on “Trial by mathematics.”. Harv. Law Rev. 84:1801–9
    [Google Scholar]
  54. George E. 2013. Steve the Bayesian. CHANCE 26:16–17
    [Google Scholar]
  55. Goldenberg A, Shmueli G, Caruana RA, Fienberg SE 2002. Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales. PNAS 99:5237–40
    [Google Scholar]
  56. Goldenberg A, Zheng AX, Fienberg SE, Airoldi EM 2010. A survey of statistical network models. Found. Trends Mach. Learn. 2:2129–233
    [Google Scholar]
  57. Gneiting T 2018. Special section in memory of Stephen E. Fienberg (1942–2016), AOAS Editor-in-Chief 2013–2015. Ann. Appl. Stat 12:2)
    [Google Scholar]
  58. Günnemann S, Faloutsos C 2013. Mixed membership subspace clustering. Proceedings of the 2013 IEEE 13th International Conference on Data Mining221–30 Red Hook, NY: Curran
    [Google Scholar]
  59. Han Q, Xu K, Airoldi E 2015. Consistent estimation of dynamic and multi-layer block models. Proceedings of the 32nd International Conference on International Conference on Machine Learning F Bach, D Blei 1511–20 Brookline, MA: Microtome
    [Google Scholar]
  60. Hanneke S, Xing EP 2007. Discrete temporal models of social networks. Statistical Network Analysis: Models, Issues, and New Directions EM Airoldi, DM Blei, SE Fienberg, A Goldenberg, EP Xing, AX Zheng 115–25 New York: Springer
    [Google Scholar]
  61. Herrnstein RJ, Murray C 1994. The Bell Curve: Intelligence and Class Structure in American Life New York: Free Press
    [Google Scholar]
  62. Kaye D. 1982. Statistical evidence of discrimination. J. Am. Stat. Assoc. 77:773–83
    [Google Scholar]
  63. Lauritzen S, Rinaldo A, Sadeghi K 2017. On exchangeability in network models. arXiv:1709.03885 [math.ST]
  64. Lindley DV. 1977. Probability and the law. J. R. Stat. Soc. D 26:203–20
    [Google Scholar]
  65. Manrique-Vallier D. 2014. Longitudinal mixed membership trajectory models for disability survey data. Ann. Appl. Stat. 8:2268–91
    [Google Scholar]
  66. Manrique-Vallier D, Fienberg SE 2008. Population size estimation using individual level mixture models. Biom. J. 50:1051–63
    [Google Scholar]
  67. Manton KG, Corder L, Stallard E 1997. Chronic disability trends in elderly United States populations: 1982–1994. PNAS 94:2593–98
    [Google Scholar]
  68. Mortera J. 2017. Issue in memory of Stephen E. Fienberg. Law Probab. Risk 16:149–50
    [Google Scholar]
  69. National Research Council 1969. National Halothane Study: A Study of the Possible Association Between Halothane Anesthesia and Postoperative Hepatic Necrosis Washington, DC: Natl. Acad. Press
    [Google Scholar]
  70. National Research Council 1995. Modernizing the U.S. Census Washington, DC: Natl. Acad. Press
    [Google Scholar]
  71. National Research Council 2003. The Polygraph and Lie Detection Washington, DC: Natl. Acad. Press
    [Google Scholar]
  72. National Research Council 2009. Strengthening Forensic Science in the United States: A Path Forward Washington, DC: Natl. Acad. Press
    [Google Scholar]
  73. Pachter L, Sturmfels B 2004a. Parametric inference for biological sequence analysis. PNAS 101:16138–43
    [Google Scholar]
  74. Pachter L, Sturmfels B 2004b. Tropical geometry of statistical models. PNAS 101:16132–37
    [Google Scholar]
  75. Petrović S, Rinaldo A, Fienberg SE 2009. Algebraic statistics for a directed random graph model with reciprocation. Algebraic Methods in Statistics and Probability II MAG Viana, HP Wynn 261–84 Providence, RI: Am. Math. Soc.
    [Google Scholar]
  76. Putinar M, Sullivant S 2009. Emerging Applications of Algebraic Geometry New York: Springer
    [Google Scholar]
  77. Rea S. 2016. Stephen E. Fienberg, 1942–2016. Carnegie Mellon University News Dec. 14. https://www.cmu.edu/news/stories/archives/2016/december/obituary-fienberg.html
    [Google Scholar]
  78. Reid N, Stigler S 2017. Memoriam: Stephen E. Fienberg 1942–2016. Annu. Rev. Stat. Appl 4iii–iv
    [Google Scholar]
  79. Rinaldo A, Fienberg SE, Zhou Y 2009. On the geometry of discrete exponential families with application to exponential random graph models. Electron. J. Stat. 3:446–84
    [Google Scholar]
  80. Rinaldo A, Petrović S, Fienberg SE 2013. Maximum likelihood estimation in the β-model. Ann. Stat. 41:1085–110
    [Google Scholar]
  81. Shmueli G, Fienberg SE 2006. Current and potential statistical methods for monitoring multiple data streams for bio-surveillance. Statistical Methods in Counter-Terrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication A Wilson, G Wilson, DH Olwell 109–40 New York: Springer
    [Google Scholar]
  82. Slavković A. 2013. Steve the matchmaker: the marriage of statistics and computer science in the world of data privacy. CHANCE 26:4
    [Google Scholar]
  83. Slavković A, Fienberg SE 2010. Algebraic geometry of 2×2 contingency tables. Algebraic and Geometric Methods in Probability and Statistics P Gibilisco, E Riccomagno, MP Rogantin, HP Wynn 67–85 Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  84. Stasi D, Sadeghi K, Rinaldo A, Petrović S, Fienberg SE 2014. β models for random hypergraphs with a given degree sequence. Proceedings of 21st International Conference on Computational Statistics M Gilli, A Nieto-Reyes, G Gonzalez-Rodriguez 593–600 Red Hook, NY: Curran
    [Google Scholar]
  85. Straf ML, Tanur JM 2013. A conversation with Stephen E. Fienberg. Stat. Sci. 28:447–63
    [Google Scholar]
  86. Tanur JM. 1982. Advances in methods for large-scale surveys and experiments. Behavioral and Social Science Research: A National Resource. Part II RM Adams, NJ Smelser, DJ Treiman Washington, DC: Natl. Acad. Press
    [Google Scholar]
  87. Tiao GC, Fienberg SE 1969. Bayesian estimation of latent roots and vectors, with special reference to the bivariate normal distribution. Biometrika 56:97–108
    [Google Scholar]
  88. Tribe LH. 1971a. Trial by mathematics: precision and ritual in the legal process. Harv. Law Rev. 84:1329–93
    [Google Scholar]
  89. Tribe LH. 1971b. The continuing debate over mathematics in the law of evidence: a further critique of mathematical proof. Harv. Law Rev. 84:1810–20
    [Google Scholar]
  90. Woodbury MA, Clive J, Garson A 1978. Mathematical typology: a grade of membership technique for obtaining disease definition. Comput. Biomed. Res 11:277–98
    [Google Scholar]
  91. Yang X, Fienberg SE, Rinaldo A 2012. Differential privacy for protecting multi-dimensional contingency table data: extensions and applications. J. Priv. Confid. 4:101–25
    [Google Scholar]
  92. Yang X, Rinaldo A, Fienberg SE 2014. Estimation for dyadic-dependent exponential random graph models. J. Algebraic Stat. 5:39–63
    [Google Scholar]
  93. Zheng AX, Goldenberg A 2006. A generative model for dynamic contextual friendship networks Work. Pap. CMU-ML-06-107, Sch. Comput Sci., Carnegie Mellon Univ Pittsburgh, PA:
    [Google Scholar]
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