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Tuesday, November 26, 2013 4:00 pm - 4:00 pm EST (GMT -05:00)

WatRISQ seminar by Steven Kou, National University of Singapore

Robust measurement of economic tail risk

We prove that the only tail risk measure that satisfies a set of economic axioms proposed by Schmeidler (1989, Econometrica) and a statistical requirement called elicitability (i.e. there exists an objective function such that a reasonable estimator must be a solution of minimizing the expected objective function) is the median shortfall, which is the median of the tail loss distribution and is also the VaR at a high confidence level.

Thursday, May 14, 2015 4:00 pm - 4:00 pm EDT (GMT -04:00)

David Sprott distinguished lecture by William Woodall, Virginia Tech

Monitoring and Improving Surgical Quality

Some statistical issues related to the monitoring of surgical quality will be reviewed in this presentation. The important role of risk-adjustment in healthcare, used to account for variations in the condition of patients, will be described. Some of the methods for monitoring quality over time, including a new one, will be outlined and illustrated with examples.

Thursday, September 24, 2015 4:00 pm - 4:00 pm EDT (GMT -04:00)

David Sprott distinguished lecture by Raymond J. Carroll, Texas A&M University

Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources.

Carroll PosterInformation from various public and private data sources of extremely large sample

Some new phenomena in high-dimensional statistics and optimization

Statistical models in which the ambient dimension is of the same order
or larger than the sample size arise frequently in different areas of
science and engineering.  Examples include sparse regression in
genomics; graph selection in social network analysis; and low-rank
matrix estimation in video segmentation.  Although high-dimensional
models of this type date back to seminal work of Kolmogorov and

Assessing financial model risk

Model risk has a huge impact on any financial or insurance risk measurement procedure and its quantification is therefore a crucial step. In this talk, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.

Uncovering the Mechanisms of General Anesthesia: Where Neuroscience Meets Statistics

General anesthesia is a drug-induced, reversible condition involving unconsciousness, amnesia (loss of memory), analgesia (loss of pain sensation), akinesia (immobility), and hemodynamic stability. I will describe a primary mechanism through which anesthetics create these altered states of arousal. Our studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness​, in the case of propofol, and we have demonstrated ​​ that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. The success of our research has depended critically on tight coupling of experiments, ​statistical signal processing​​ and mathematical modeling.

A Machine Learning Approach to Portfolio Risk Management

Risk measurement, valuation and hedging form an integral task in portfolio risk management for insurance companies and other financial institutions. Portfolio risk arises because the values of constituent assets and liabilities change over time in response to changes in the underlying risk factors. The quantification of this risk requires modeling the dynamic portfolio value process. This boils down to compute conditional expectations of future cash flows over long time horizons, e.g., up to 40 years and beyond, which is computationally challenging. 

This lecture presents a framework for dynamic portfolio risk management in discrete time building on machine learning theory. We learn the replicating martingale of the portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is in closed form thanks to a suitable choice of the reproducing kernel Hilbert space. We develop an asymptotic theory and prove
convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. As application we compute the value at risk and expected shortfall of the one-year loss of some stylized portfolios.

Monday, January 20, 2020 10:00 am - 10:00 am EST (GMT -05:00)

Department seminar by Jared Huling, Ohio State University

Sufficient Dimension Reduction for Populations with Structured Heterogeneity

Risk modeling has become a crucial component in the effective delivery of health care. A key challenge in building effective risk models is accounting for patient heterogeneity among the diverse populations present in health systems. Incorporating heterogeneity based on the presence of various comorbidities into risk models is crucial for the development of tailored care strategies, as it can provide patient-centered information and can result in more accurate risk prediction. Yet, in the presence of high dimensional covariates, accounting for this type of heterogeneity can exacerbate estimation difficulties even with large sample sizes. Towards this aim, we propose a flexible and interpretable risk modeling approach based on semiparametric sufficient dimension reduction. The approach accounts for patient heterogeneity, borrows strength in estimation across related subpopulations to improve both estimation efficiency and interpretability, and can serve as a useful exploratory tool or as a powerful predictive model. In simulated examples, we show that our approach can improve estimation performance in the presence of heterogeneity and is quite robust to deviations from its key underlying assumption. We demonstrate the utility of our approach in the prediction of hospital admission risk for a large health system when tested on further follow-up data.