David Ruppert
Biography
David Ruppert is Andrew Schulz Jr. Professor of Engineering, School of Operations Research and Information Engineering, and Professor of Statistics and Data Science, Cornell University. He received a BA in Mathematics from Cornell University in 1970, an MA in Mathematics from the University of Vermont in 1973, and a PhD in Statistics and Probability from Michigan State University in 1977. He was Assistant and then Associate Professor of Statistics at the University of North Carolina, Chapel Hill, from 1977 to 1987. He is a Fellow of the ASA and IMS and received the Wilcoxon Prize in 1986. Professor Ruppert was named "Highly cited" researcher by ISIHighlyCited.com and was ranked 21st in mathematics by journal citations. He has had 34 PhD students, many of them now leading researchers. Professor Ruppert has worked on stochastic approximation, transformations and weighting in regression, and smoothing. His current research focuses on astrostatistics, neuroscience, measurement error models, splines, functional data analysis, semiparametric regression, causal inference, and environmental statistics. He has published over 160 articles in refereed journals and has published six books, Transformation and Weighting in Regression, Measurement Error in Nonlinear Models (first and second editions), Semiparametric Regression, Statistics and Finance: An Introduction, Statistics and Data Analysis for Financial Engineering (first and second edition), Semiparametric Regression with R.
Research Interests
Professor Ruppert's current research is on calibration and uncertainty analysis, semiparametric regression, splines in statistics, functional data analysis, astrostatistics, biostatistics, fMRI and ICA. He has had continuous external research funding since 1978 with grants from NSF, NIH, and EPA. He has published over 125 research papers.
Teaching Interests
In the past, Professor Ruppert has taught courses on regression, experimental design, measurement error, Taguchi methods, nonparametric estimation, time series, data mining, mathematical statistics, rank tests, statistical methods for risk analysis, and asymptotic theory. More recently, he has developed Ph.D. courses on Bayesian statistics and functional data analysis and teaches two courses for undergraduates and master’s students—ORIE 4630/5630, Operations Research Tools for Financial Engineering (which uses Professor Ruppert's textbook, Statistics and Finance: An Introduction) and ORIE 5640, Statistics for Financial Engineering (which uses Professor Ruppert's textbook Statistics and Data Analysis for Financial Engineering).
Service Interests
Professor Ruppert was Editor of the Electronic Journal of Statistics 2010-2012 and was Co-editor of the Journal of the American Statistical Association-Theory & Methods 2014-2017.
Selected Publications
- Xiao, Luo., Y. Li, David Ruppert. 2013. "Fast Bivariate P-splines: the Sandwich Smoother." JRSS-B 75: 577-599.
- Ruppert, David, David S. Matteson. 2015. Statistics and Data Analysis for Financial Engineering, 2nd Edition.
- Risk, B B., D S. Matteson, R N. Spreng, D Ruppert. 2016. "Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments." NeuroImage 142: 280-292.
- Kowal, D., D. Matteson, David Ruppert. 2016. "A Bayesian Functional Dynamic Linear Model." Journal of the American Statistical Association.
- Yang, Ran, Kent, David, Apley, Daniel, Staum, Jeremy, Ruppert, David (2021) Bias-corrected Estimation of the Density of a Conditional Expectation in Nested Simulation Problems, ACM Transactions on Modeling and Computer Simulation, 31, 1-36.
- Kent, David, and Ruppert, David (2023) Smoothness-Penalized Deconvolution (SPeD) of a Density Estimate, JASA, to appear.
Selected Awards and Honors
- Distinguished Alumni Award (Department of Statistical Science, Cornell University) 2014
- "Highly cited" researcher (Wilcoxon Award 1986 - ISIHighlyCited.com) 2010
- Ranked #21 by citations in mathematics (Essential Science Indicators) 2010
- Fellow of the Institute of Mathematical Statistics (1986) and American Statistical Association 1989
- Wilcoxon prize for best practical applications paper (Technometrics) 1986
Education
- B.A. (Mathematics, General), Cornell University, 1970
- M.A. (Statistics, Math & Theoretical), University of Vermont, 1973
- Ph.D. (Statistics, Math & Theoretical), Michigan State University, 1977