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
Nov 16, 2017
Oct 27, 2017
Oct 24, 2017
Aug 28, 2017
Speaker: Nicholas Gunther, UC Berkeley (Speaker - Featured)
We estimate the financing rate implicit in equity index futures (“FIR”) by comparing the prices of the near and next contracts and adjusting for expected dividends and convexity. We provide a direct estimate of the FIR volatility, along with the correlation of the FIR and the underlying stock index, which are required for the convexity adjustment and the specification of confidence intervals. Our...
Speaker: (Speaker - Featured)
Le Chen, University of Nevada, Las Vegas
In this talk, I will present some recent progress in understanding the existence, regularity and strict positivity of the (joint-) density of the solution to a semilinear stochastic heat equation. The talk will consists two parts. In the first part, I will show that under a mild cone condition for the diffusion coefficient, one can establish the smooth joint density at multiple points. The tool...
Michael Hudgens, UNC-Chapel Hill
A fundamental assumption usually made in causal inference is that of no interference between individuals (or units), i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected depends on who...
Somayeh Sojoudi, EECS, Mechanical Engineering (Speaker)
Learning models from data has a significant impact on many disciplines, including computer vision, medical imaging, social networks, neuroscience and signal processing. In the network inference problem, one may model the relationships between the network components through an underlying inverse covariance matrix. Learning this graphical model is often challenged by the fact that only a small...
Data-Driven Methods for Learning Sparse Graphical Models