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James-Stein for eigenvectors: reducing the optimization bias in Markowitz portfolios

Lisa Goldberg, UC Berkeley

Abstract: We identify and reduce bias in the leading sample eigenvector of a high-dimensional covariance matrix of correlated variables. Our analysis illuminates how error in an estimated covariance matrix corrupts optimization. It may be applicable in finance, machine learning and genomics. Biography: Lisa Goldberg is Head of Research at Aperio and Managing Director at BlackRock.  She is Professor of the Practice of Economics at University of California, Berkeley, where she co-directs the Center for Data Analysis in Risk, an industry…

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Free Discontinuity Design (joint w/ David van Dijcke)

Florian Gunsilius, University of Michigan

Abstract: Regression discontinuity design (RDD) is a quasi-experimental impact evaluation method ubiquitous in the social- and applied health sciences. It aims to estimate average treatment effects of policy interventions by exploiting jumps in outcomes induced by cut-off assignment rules. Here, we establish a correspondence between the RDD setting and free discontinuity problems, in particular the celebrated Mumford-Shah model in image segmentation. The Mumford-Shah model is non-convex and hence admits local solutions in general. We circumvent this issue by relying on…

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IDSS Celebration

This celebratory event reflects on the impact in research and education the Institute for Data, Systems, and Society has had since its launch in 2015 and explores future opportunities with thought leaders and policy experts. In panels and plenary talks, we will discuss the impact of research areas utilizing the available massive data, in-depth understanding of underlying social and engineering systems, and the investigation of social and institutional behavior to provide answers to critical and complex challenges.

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