FDS Statistics & Data Science Seminar
“Multivariate nonparametric regression using mixed partial derivatives”
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Speaker: Adityanand Guntuboyina (Berkeley) Associate Professor, Department of Statistics University of California, Berkeley Monday, October 21, 2024 3:30 PM – Pre-talk meet and greet teatime at 219 Prospect Street, 13th floor.
There will be light snacks and beverages in the kitchen area. and via Zoom: https://yale.zoom.us/j/94223816617 |
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Information and Abstract: I will describe methods for multivariate nonparametric estimation based on constraining mixed partial derivatives. The resulting estimators are efficiently computable and work well in practice. They are also theoretically attractive when the underlying assumptions are satisfied, achieving rates of convergence that avoid the usual curse of dimensionality. Specific examples of this methodology include: MARS via LASSO, totally convex regression, Hardy-Krause variation constrained regression, and Entirely Monotonic Regression. Most of this presentation is based on joint work with Dohyeong Ki (UC Berkeley).
Bio: Adityanand Guntuboyina is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He joined Berkeley in January 2012 following a postdoctoral position at the Wharton Statistics Department, University of Pennsylvania. He earned his Ph.D. in Statistics from Yale University in 2011 under the supervision of Professor David Pollard. Prior to his doctoral studies, he completed his B.Stat and M.Stat degrees from the Indian Statistical Institute, Kolkata.
Professor Guntuboyina’s research focuses on nonparametric and high-dimensional statistics, shape-constrained statistical estimation, and Bayesian and Empirical Bayes methods. His work contributes to both the theoretical and applied aspects of statistics, addressing fundamental challenges in modern data analysis.