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
Oct 5, 2016
Speaker: Paul Kaplan, Morningstar (Speaker - Featured)
Abstract: This paper presents a formal model for theory of popularity as laid out informally by Idzorek and Ibbotson in their seminal paper, “Dimensions of Popularity (Journal of Portfolio Management, 2014). The paper does this by extending the capital asset pricing model (CAPM) to include security characteristics that different investors regard differently. This leads to an equilibrium in...
David Aldous, U. C. Berkeley
Plant differently colored points in the plane; then let random points (``Poisson rain") fall, and give each new point the color of the nearest existing point. Previous investigation and simulations strongly suggest that the colored regions converge (in some sense) to a random partition of the plane. We prove a weak version of this.
Karl Rohe, Department of Statistics, University of Wisconsin, Madison
Web crawling, snowball sampling, and respondent-driven sampling (RDS) are three types of network driven sampling techniques that are popular when it is difficult to contact individuals in the population of interest. This talk will show that if participants refer too many other participants, then under the standard Markov model in the RDS literature, the standard approaches do not provide "square...
Speaker: Terrence Hendershott, UC Berkeley (Speaker - Featured)
Wenpin Tang, U.C. Berkeley
The "Up the River" problem was formulated by Aldous (2002), where a unit drift is distributed among a finite collection of Brownian particles on R+, which are annihilated once they reach the origin. Starting K particles at x = 1, we prove Aldous’ conjecture that the push-the-laggard strategy of distributing the drift asymptotically (as K → ∞) maximizes the total number of surviving...