Statistical Science

A Conversation with Robert E. Kass

Sam Behseta

Full-text: Open access

Abstract

Rob Kass has been been on the faculty of the Department of Statistics at Carnegie Mellon since 1981; he joined the Center for the Neural Basis of Cognition (CNBC) in 1997, and the Machine Learning Department (in the School of Computer Science) in 2007. He served as Department Head of Statistics from 1995 to 2004 and served as Interim Co-Director of the CNBC 2015–2018. He became the Maurice Falk Professor of Statistics and Computational Neuroscience in 2016.

Kass has served as Chair of the Section for Bayesian Statistical Science of the American Statistical Association, Chair of the Statistics Section of the American Association for the Advancement of Science, founding Editor-in-Chief of the journal Bayesian Analysis and Executive Editor of Statistical Science. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics and the American Association for the Advancement of Science. He has been recognized by the Institute for Scientific Information as one of the 10 most highly cited researchers, 1995–2005, in the category of mathematics. Kass is the recipient of the 2017 Fisher Award and lectureship by the Committee of the Presidents of the Statistical Societies. This interview took place at Carnegie Mellon University in November 2017.

Article information

Source
Statist. Sci., Volume 34, Number 2 (2019), 334-348.

Dates
First available in Project Euclid: 19 July 2019

Permanent link to this document
https://projecteuclid.org/euclid.ss/1563501645

Digital Object Identifier
doi:10.1214/18-STS691

Mathematical Reviews number (MathSciNet)
MR3983332

Zentralblatt MATH identifier
07110700

Keywords
Statistical training Bayesian statistics statistics in neuroscience academic life statistical narrative

Citation

Behseta, Sam. A Conversation with Robert E. Kass. Statist. Sci. 34 (2019), no. 2, 334--348. doi:10.1214/18-STS691. https://projecteuclid.org/euclid.ss/1563501645


Export citation

References

  • Berger, J. O. and Delampady, M. (1987). Testing precise hypotheses. Statist. Sci. 3 317–352.
  • Berger, J. O. and Selke, T. (1987). Testing a point null hypothesis: irreconcilability of P values and evidence. With comments and a rejoinder by the authors. J. Amer. Statist. Assoc. 82 112–139.
  • Cramér, H. (1973). The Elements of Probability Theory and Some of Its Applications. Wiley, New York.
  • DeGroot, M. H. (1986). A conversation with David Blackwell. Statist. Sci. 1 40–53.
  • Feller, W. (1950). An Introduction to Probability Theory and Its Applications. Vol. I. Wiley, New York, NY.
  • Feller, W. (1957). An Introduction to Probability Theory and Its Applications. Vol. II, 2nd ed. Wiley, New York.
  • Ferguson, T. S. (1967). Mathematical Statistics: A Decision Theoretic Approach. Probability and Mathematical Statistics, Vol. 1. Academic Press, New York.
  • Jarosiewicz, B. Chase, S. M. Fraser, G. W. Velliste, M. Kass, R. E. and Schwartz, A. B. (2008). Functional network reorganization during learning in a brain-machine interface paradigm. Proc. Natl. Acad. Sci. USA 105 19486–19491.
  • Kass, R. E. (1989). The geometry of asymptotic inference. Statist. Sci. 4 188–234. With comments and a rejoinder by the author.
  • Kass, R. E., Eden, U. T. and Brown, E. N. (2014). Analysis of Neural Data. Springer Series in Statistics. Springer, New York.
  • Kass, R. E., Kelly, R. C. and Loh, W.-L. (2011). Assessment of synchrony in multiple neural spike trains using loglinear point process models. Ann. Appl. Stat. 5 1262–1292.
  • Kass, R. E. and Raftery, A. E. (1995). Bayes factors. J. Amer. Statist. Assoc. 90 773–795.
  • Kass, R. E. and Steffey, D. (1989). Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes models). J. Amer. Statist. Assoc. 84 717–726.
  • Kass, R., Ventura, V. and Brown, E. N. (2005). Statistical issues in the analysis of neuronal data. J. Neurophysiol. 1 8–25.
  • Kass, R. E. and Vos, P. W. (1997). Geometrical Foundations of Asymptotic Inference. Wiley Series in Probability and Statistics: Probability and Statistics. Wiley, New York.
  • Kass, R. E. and Wasserman, L. A. (1996). The selection of prior distributions by formal rules. J. Amer. Statist. Assoc. 91 1343–1370.
  • Kass, R. E., Caffo, B. S., Davidian, M., Meng, X.-L., Yu, B. and Reid, N. (2016). Ten simple rules for effective statistical practice. PLoS Comput. Biol. 12 e1004961.
  • Kendall, M. and Stuart, A. (1977). The Advanced Theory of Statistics: Distribution Theory. Vol. 1, 4th ed. Macmillan Publishing, New York.
  • Mosteller, F. and Tukey, J. (1968). Data analysis, including statistics. In Handbook of Social Psychology, 2nd ed. (G. Lindzey and E. Aronson, eds.) 2. Wiley, New York.
  • Mosteller, F. and Wallace, D. L. (1964). Inference and Disputed Authorship: The Federalist. Addison-Wesley, Reading, MA.
  • Raftery, A. (2001). Statistics in the Twenty First Century, 1st ed. CRC Press, New York.
  • Rao, C. R. (1973). Linear Statistical Inference and Its Applications, 2nd ed. Wiley, New York.
  • Sellke, T., Bayarri, M. J. and Berger, J. O. (2001). Calibration of $p$ values for testing precise null hypotheses. Amer. Statist. 55 62–71.
  • Tierney, L. (1994). Markov chains for exploring posterior distributions. Ann. Statist. 22 1701–1762.