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376 Statistics in a new eraof the network. After an overview of the extensive literature in statistics andengineering on sequential change-point detection and estimation, statisticalprocess control, and stochastic adaptive control, it discusses how these methodscan be modified and further developed for network models to come upwith early warning indicators for financial instability and systemic failures.This is an ongoing research project with colleagues at the Financial and RiskModeling Institute at Stanford, which is an interdisciplinary research centerinvolving different schools and departments.Besides risk management, the field of statistics has played an importantrole in algorithmic trading and quantitative investment strategies, which havegained popularity after the financial crisis when hedge funds employing thesestrategies outperformed many equity indices and other risky investment options.Statistical modeling of market microstructure and limit-order book dynamicsis an active area of research; see for example, Ait-Sahalia et al. (2005)and Barndorff-Nielsen et al. (2008). Even the foundational theory of meanvarianceportfolio optimization has received a new boost from contemporaneousdevelopments in statistics during the past decade. A review of thesedevelopments is given by Lai et al. (2011) who also propose a new statisticalapproach that combines solution of a basic stochastic optimization problemwith flexible modeling to incorporate time series features in the analysis ofthe training sample of historical data.33.6 ConclusionThere are some common threads linking statistical modeling in finance andhealth care for the new era. One is related to “Big Data” for regulatory supervision,risk management and algorithmic trading, and for emerging healthcare systems that involve electronic medical records, genomic and proteomicbiomarkers, and computer-assisted support for patient care. Another is relatedto the need for collaborative research that can integrate statistics with domainknowledge and subject-matter issues. A third thread is related to dynamicpanel data and empirical Bayes modeling in finance and insurance. Healthinsurance reform is a major feature of the 2010 Affordable Care Act, and hasled to a surge of interest in the new direction of health insurance in actuarialscience. In the US, insurance contracts are predominantly fee-for-service(FFS). In such arrangements the FFS contracts offer perverse incentives forproviders, typically resulting in over-utilization of medical care. Issues withFFS would be mitigated with access to more reliable estimates of patient heterogeneitybased on administrative claims information. If the health insurercan better predict future claims, it can better compensate providers for caringfor patients with complicated conditions. This is an area where the field ofstatistics can have a major impact.