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UID:643@fds.yale.edu
DTSTART;TZID=America/New_York:20241021T160000
DTEND;TZID=America/New_York:20241021T170000
DTSTAMP:20240924T165510Z
URL:https://fds.yale.edu/events/sds-seminar-adityanand-guntuboyina-berkele
y-multivariate-nonparametric-regression-using-mixed-partial-derivatives/
SUMMARY:S&\;DS Seminar: Adityanand Guntuboyina (Berkeley)\, "Multivariat
e nonparametric regression using mixed partial derivatives"
DESCRIPTION:Information and Abstract: I will describe methods for multivar
iate nonparametric estimation based on constraining mixed partial deriv
atives. The resulting estimators are efficiently computable and work well
in practice. They are also theoretically attractive when the underlying as
sumptions are satisfied\, achieving rates of convergence that avoid the us
ual curse of dimensionality. Specific examples of this methodology include
: MARS via LASSO\, totally convex regression\, Hardy-Krause variation cons
trained regression\, and Entirely Monotonic Regression. Most of this prese
ntation is based on joint work with Dohyeong Ki (UC Berkeley). \n\n\n\nBi
o: Adityanand Guntuboyina is an Associate Professor in the Department of S
tatistics at the University of California\, Berkeley. He joined Berkeley i
n January 2012 following a postdoctoral position at the Wharton Statistics
Department\, University of Pennsylvania. He earned his Ph.D. in Statistic
s from Yale University in 2011 under the supervision of Professor David Po
llard. Prior to his doctoral studies\, he completed his B.Stat and M.Stat
degrees from the Indian Statistical Institute\, Kolkata.\n\n\n\nProfessor
Guntuboyina's research focuses on nonparametric and high-dimensional stati
stics\, shape-constrained statistical estimation\, and Bayesian and Empiri
cal Bayes methods. His work contributes to both the theoretical and applie
d aspects of statistics\, addressing fundamental challenges in modern data
analysis.\n\n\n\nWebsite\n
ATTACH;FMTTYPE=image/jpeg:https://fds.yale.edu/wp-content/uploads/2024/09/
Adityanand_Guntuboyina.jpg
CATEGORIES:Statistics & Data Science Seminar
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