Annual Report 2008.pdf - SAMSI

selection problem and the diversification effects via the secondary risk indicator in a multivariate
Extreme Value Theory framework without assuming any parametric dependence structure. We
propose a portfolio selection procedure to construct the optimal portfolio. An empirical study
illustrates the method.
Francis Zwiers
Environment Canada Department: Climate Research Division
Francis.Zwiers@ec.gc.ca
“Analysis of Extremes in Climate Science”
Extremes in the climate system occur on all space and time scales, ranging from very short
duration events that affect small localized areas, such as tornadoes, to events such as droughts
that may affect very large areas for extended periods of time. In this talk I will briefly review a
range of approaches to the analysis of extremes that are used in climate science and will discuss
some of the statistical issues that arise. The approaches range from the analysis of simple time
series of threshold crossing frequencies where the thresholds represent moderately unusual be
not particularly extreme values (e.g., the 90 th percentile of daily maximum temperature), to
analyses using extreme value theory, to consideration of individual very high impact events that
are unprecedented in the instrumental climate record.
POSTER ABSTRACTS
Matthias Degen
Eidgenossiche Technische Hochschule Zurich
degen@math.ethz.ch
“Issues in the Estimation of Risk Capital for Operational Risk”
Hongfei Li
IBM T. J. Watson Research Center
liho@us.ibm.com
“Spatial-temporal Modeling for Prediction of Storm Outages”
Severe weather conditions, such as hurricanes, thunder storms, have a major impact on electric
utility equipment failure and power outages. An accurate prediction can effectively help to plan
emergency management of crews and power restoration. We proposed a spatial-temporal
modeling for predicting outages caused by severe weather and applied our model to the outage
data provided by a utility electric company in New York. Given weather predicted by IBM Deep,
we modeled the outages by Poisson regression while including spatial and temporal correlation
in the covariance structure. In addition, the number of customers interrupted and the time to
restoration are also predicted following the outages predicted.
Keywords: spatial-temporal modeling, outage prediction, Poisson regression model
Veder Madar