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.
Statistics at UC Berkeley
Mar 15, 2018
Mar 6, 2018
Feb 8, 2018
Sourav Sarkar, U.C. Berkeley
Competitive erosion models a random interface sustained in equilibrium by equal and opposite pressures on each side of the interface. Here we study the following one dimensional version. Begin with all sites of Z uncolored. A blue particle performs simple random walk from 0 until it reaches a nonzero red or uncolored site, and turns that site blue; then, a red particle performs simple random...
Joel Middleton, UC Berkeley
Under the Neyman causal model, a well-known result is that OLS with treatment-by-covariate interactions cannot harm asymptotic precision of estimated treatment effects in completely randomized experiments. But do such guarantees extend to experiments with more complex designs? This paper proposes a general framework for addressing this question and defines a class of generalized regression...
Speaker: Jake Soloff, UC Berkeley (Speaker - Featured)
Agostino Capponi, Columbia University
Agostino Capponi joined Columbia University's IEOR Department in August 2014, where he is also a member of the Institute for Data Science and Engineering. His main research interests are in the area of networks, with a special focus on systemic risk, contagion, and control. In the context of financial networks, the outcome of his research contributes to a better understanding of risk...
Solving composite minimization problems arising in statistics and engineering, with applications to phase retrieval
John C. Duchi, Stanford University
We consider minimization of stochastic functionals that are compositions of a (potentially) non-smooth convex function h and smooth function c. We develop two stochastic methods--a stochastic prox-linear algorithm and a stochastic (generalized) sub- gradient procedure--and prove that, under mild technical conditions, each converges to stationary points of the stochastic objective. Additionally,...