Statistics at UC Berkeley

4th Annual CDAR Symposium 2018 (Group)
Oct 19, 2018 8:30am University Club Memorial Stadium
Our conference will feature new developments in data science, highlighting applications to finance and risk management. Confirmed speakers include Jeff Bohn, Olivier Ledoit, Ulrike Malmendier, Steven Kou, Ezra Nahum, Roy Henriksson, and Ken Kroner.
Speaker: Samim Ghamami, Goldman Sachs (Speaker - Featured)
Oct 23, 2018 11:00am 1011 Evans Hall
Speaker: Xiang Zhang, SWUFE (Speaker - Featured)
Oct 23, 2018 11:00am 1011 Evans Hall
Recent research finds that prominent asset pricing models have mixed success in evaluating the cross-section of anomalies, which highlights proliferation of anomalies and zoo of factors. In this paper, I investigate that how is the relative pricing performance of these models to explain anomalies, when comparing their misspecification errors– the Hansen–Jagannathan (HJ) distance measure. I find...
Anca Dragan, UC Berkeley (Speaker - Featured)
Oct 23, 2018 4:10pm 190 Doe Library
Estimation, planning, control, and learning are giving us robots that can generate good behavior given a specified objective and set of constraints. What I care about is how humans enter this behavior generation picture, and study two complementary challenges: 1) how to optimize behavior when the robot is not acting in isolation, but needs to coordinate or collaborate with people; and 2) what to...
Berkeley Distinguished Lectures in Data Science
Alex Dunlap, Stanford University
Oct 24, 2018 3:00pm 1011 Evans Hall
The (d+1)-dimensional KPZ equation \[ \partial_t h = \nu \Delta h + \frac{\lambda}{2}|\nabla h|^2 + \sqrt{D}\dot{W}, \] in which \dot{W} is a space--time white noise, is a natural model for the growth of d-dimensional random surfaces. These surfaces are extremely rough due to the white noise forcing, which leads to difficulties in interpreting the nonlinear term in the equation. In...

Statistics at UC Berkeley: We are a community engaged in research and education in probability and statistics. In addition to developing fundamental theory and methodology, we are actively involved in statistical problems that arise in such diverse fields as molecular biology, geophysics, astronomy, AIDS research, neurophysiology, sociology, political science, education, demography, and the U.S. Census. We have forged strong interdisciplinary links with other departments and areas of study, particularly biostatistics, mathematics, computer science, and biology, and actively seek to recruit graduate students and faculty who can help to build and maintain such links. We also offer a statistical consulting service each semester.