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 8, 2014
Jul 7, 2016
Statistics alumna Tamara Broderick (Ph.D. 2014) received the 2015 Savage Award and the ISBA Lifetime Members Junior Researchers Award.
Jun 29, 2016
Speaker: Alex Papanicolaou, UC Berkeley (Speaker - Featured)
Abstract: We provide a simple and easy to use goodness-of-fit test for the misspecification of the volatility function in diffusion models. The test uses power variations constructed as functionals of discretely observed diffusion processes.
Fraydoun Rezakhanlou, UC Berkeley
Hamilton-Jacobi PDEs were originally formulated to study Hamiltonian ODEs. It turns out that many growth processes can be microscopically modeled by Hamilton-Jacobi equations associated with stochastic Hamiltonian functions. Macroscopic descriptions can be achieved by appropriate scaling limits. The passage from the microscopic details to the macroscopic equation is related to the homogenization...
Alex Papanicolaou, Consortium for Data Analytics in Risk
Abstract. Financial markets produce massive amounts of complex data from multiple agents, and analyzing these data is important for building an understanding of markets, their formation, and the influence of different trading strategies. We utilize a signal processing approach to deal with these complexities by applying background subtraction methods to high frequency financial data to extract...
Ash Alizadeh, Stanford University School of Medicine, David Matthew Kurtz, Stanford University School of Medicine
Predicting an individual's response to treatment remains a major challenge in clinical oncology. Current methods rely on clinical and biological risk factors identified prior to therapy, such as tumor stage, histological grade, or tumor genotype. These factors are associated with differences in response and survival in the population; however, their ability to predict outcome for an individual...
Seminar 217, Risk Management: Stochastic Intensity Margin Modeling of Credit Default Swap Portfolios
Baeho Kim (Joint with Samim Ghamami and Dong Hwan Oh), UC Berkeley (Speaker - Featured)
Abstract: We consider the problem of initial margin (IM) modeling for portfolios of credit default swaps (CDS) from the perspective of a derivatives Central Counterparty (CCP). The CCPs' IM models in practice are based on theoretically-unfounded direct statistical modeling of CDS spreads.