Statistical Methodology and Applications to Engineering and Economics

Stanford University, Stanford CA

Investigators

Abstract

The past three years witnessed the beginning of a new era in financial markets and in the US health care system, following the health care and financial reform legislation in 2010. A long-term objective of the research is to develop innovative statistical methodologies and to combine them with advances in high-performance computing and communication networks for addressing the challenges in quantitative finance and health care in this new era. The research will lead to advances and innovations in statistical methods in biomedicine, economics and engineering, paving the way for timely applications to clinical and translational medical research, health care, homeland security, environmental change, and risk management. A broader impact of this research is the training of the next generation of scientists in academia, industry, and government, by involving graduate students in all phases of the research, and by developing new course material built around the research and its applications. The research projects can be broadly divided into four areas. The first is multi-arm bandits with covariates, also called "contextual bandits" in machine learning, and their applications to personalized strategies in medicine and electronic business, in particular, to genomic-guided personalized cancer treatments and biomarker- guided treatments for depression in neuroscience. The second area is fault detection and surveillance in network models for manufacturing systems, for systemic risk in financial markets, for homeland security, and for environment or global change. The third area is the development of efficient adaptive particle filters in nonlinear state-space models that have far-ranging applications in engineering and economics, with robotics and stochastic adaptive control as the main focus in this research. The fourth area includes dynamic empirical Bayes modeling of joint default risk for multiple firms in credit markets, of loan loss risk in retail banking and of insurance claims, macroeconomic time series modeling and forecasting, and data analytics for health care cost and preventive management.