Kass's early research was on differential geometry in statistics,[2] which formed the basis for his book Geometrical Foundations of Asymptotic Inference[3] (with Paul Vos), and on Bayesian methods. Since 2000 his research has focused on statistical methods in neuroscience.
Kass's best-known work includes a comprehensive re-evaluation of Bayesian hypothesis testing and model selection,[4][5] and the selection of prior distributions,[6] the relationship of Bayes and Empirical Bayes methods,[7] Bayesian asymptotics,[8][9] the application of point process statistical models to neural spiking data,[10][11] the challenges of multiple spike train analysis,[12][13] the state-space approach to brain-computer interface,[14] and the brain's apparent ability to solve the credit assignment problem during brain-controlled robotic movement.[15] Kass's book Analysis of Neural Data[16] (with Emery Brown and Uri Eden) was published in 2014. Kass has also written on statistics education and the use of statistics, including the articles, "What is Statistics?",[17] "Statistical Inference: The Big Picture,"[18] and "Ten Simple Rules for Effective Statistical Practice".[19]
Professional and administrative activities
Kass has served 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 Bayesian Analysis (journal), and Executive Editor (editor-in-chief) of the international review journal Statistical Science. At Carnegie Mellon University he was Department Head of Statistics from 1995 to 2004 and Interim Co-director of the joint CMU–University of Pittsburgh Center for the Neural Basis of Cognition 2015–2018.[20][21]
Honors
Kass is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science, and an elected member of the National Academy of Sciences.[22] For his work on statistical modeling of neural synchrony,[23] in 2013 he received the Outstanding Statistical Application Award from the American Statistical Association, and in 2017 he received the R.A. Fisher Award and Lectureship, now known as the COPSS Distinguished Achievement Award and Lectureship, from the Committee of Presidents of Statistical Societies.
↑ Kass, Robert E.; Raftery, Adrian (2012-02-27). "Bayes Factors". Journal of the American Statistical Association. 90 (430): 773–795. doi:10.1080/01621459.1995.10476572.
↑ Kass, Robert E., Tierney, Richard L. and Kadane, Joseph B. (1990) The validity of posterior expansions based on Laplace's method, Essays in Honor of George Bernard, eds. S. Geisser, J.S. Hodges, S.J. Press, and A. Zellner, Amsterdam: North Holland, 473-488.
↑ DiMatteo, Illaria; Genovese, Christopher R.; Kass, Robert E. (2001-12-01). "Bayesian curve-fitting with free-knot splines". Biometrika. 88 (4): 1055–1071. doi:10.1093/biomet/88.4.1055.
↑ Brown, Emery N.; Mitra, Partha P.; Kass, Robert E. (2004-04-27). "Multiple neural spike train data analysis: state-of-the-art and future challenges". Nature Neuroscience. 7 (4): 456–461. doi:10.1038/nn1228. PMID15114358. S2CID562815.
↑ Kass, Robert E.; Ventura, Valerie; Brown, Emery N. (2005-07-01). "Statistical Issues in the Analysis of Neuronal Data". Journal of Neurophysiology. 94 (1): 8–25. doi:10.1152/jn.00648.2004. PMID15985692.
↑ Brockwell, Anthony E.; Rojas, A.L.; Kass, Robert E. (2004-04-01). "Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering". Journal of Neurophysiology. 91 (4): 1899–1907. doi:10.1152/jn.00438.2003. PMID15010499. S2CID15092944.
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