Statistics at UC Berkeley

Marco Avellaneda, New York University (Speaker)
Sep 22, 2020 11:00am Online
Abstract:
ABSTRACT: Modeling return correlations between thousands of stocks poses great challenges, as empirical estimators tend to perform poorly when assets don’t share common risk factors, such as country or industry sector. In this paper, we show the advantages of using Hierarchical Principal Component Analysis (HPCA) for modeling correlations, as opposed to the classic PCA. Furthermore, we propose a...
Sam Pimentel, UC Berkeley, Weijie Su, University of Pennsylvania
Sep 23, 2020 1:30pm Evans Hall
Abstract:
Title: Local Elasticity: A Phenomenological Approach Toward Understanding Deep Learning Speaker: Weijie Su Title: The uniform general signed rank test and its design sensitivity Speaker: Sam Pimentel
Balint Virag, U Toronto
Sep 23, 2020 3:10pm Zoom link: https://berkeley.zoom.us/j/93030418340 Evans Hall
Abstract:
The directed landscape is the conjectured universal scaling limit of the most common random planar metrics. The limit laws of distances of objects are given by the KPZ fixed point. We show that the KPZ fixed point is characterized by the Baik Ben-Arous Peche statistics well-known from random matrix theory. This provides a general and elementary method for showing convergence to the KPZ...
Ola Mahmoud, University of Basel (Speaker)
Sep 29, 2020 11:00am Online
Abstract:
ABSTRACT: We present evidence that sustainability is inextricably linked with market-implied uncertainty. We derive an econometric decomposition of sustainability ratings yielding three orthogonal components capturing uncertainty, investor sentiment, and an idiosyncratic sustainability factor. Examining the shock of the COVID-19 pandemic to the US stock market in light of these explanatory...
Mark Risser, Lawrence Berkeley National Laboratory
Sep 30, 2020 4:00pm Evans Hall
Abstract:
Abstract: In spite of the diverse literature on nonstationary spatial modelling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for moderately sized nonstationary spatial data sets on a personal laptop. For statisticians and data scientists who wish to conduct posterior inference and prediction with appropriate uncertainty...

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.