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SUMMARY:Post-Conference Course: A Short Course on Extreme Value Statistics
in Applications
DTSTART;VALUE=DATE-TIME:20220630T070000Z
DTEND;VALUE=DATE-TIME:20220630T110000Z
DTSTAMP;VALUE=DATE-TIME:20220817T103222Z
UID:indico-event-24@conferences.enbis.org
DESCRIPTION:\n\nA Short Course on Extreme Value Statistics \nin Applicati
ons.\n\nPart of the ENBIS-22 Trondheim conference.\n\n\nBy Arvid Naess\, D
ept of Mathematical Sciences\, NTNU\, Trondheim\, Norway\n\n\nThis short c
ourse aims at developing the ability to carry out an extreme value analysi
s on the basis of observed or simulated time histories arising from random
processes observed e.g as a result of environmental phenomena. Rational m
ethodologies that make it possible to predict extremes of e.g. wind speeds
\, wave heights\, water levels in rivers\, rainfalls etc. are clearly high
ly desirable\, as they would be in many other areas. The emphasis will be
on the prediction of extremes in short-term\, stationary environmental con
ditions. However\, because of its importance\, the prediction of long-term
extremes will also be discussed\n\n\nThe course schedule will consist of
four 45 minutes lectures with three 15 minutes breaks for refreshments and
coffee/tea. The first lecture starts at 9:15\, and the last ends at 13:00
.\n\n\nCourse specifics: The course will start with a discussion of the co
mmonly adopted approaches to extreme value prediction\, which\, even in ap
plications\, have very often been based on asymptotic results. This is don
e either by assuming that epochal extremes\, e.g. three hours extreme valu
es\, are distributed according to the generalized (asymptotic) extreme val
ue distribution with unknown parameters to be estimated on the basis of th
e observed data. Or\, it is assumed that the exceedances above high thresh
olds follow a generalized (asymptotic) Pareto distribution with parameters
to be estimated from the data. The major problem with such approaches is
that the asymptotic extreme value theory itself cannot be used in practice
to decide to what extent it is applicable for the observed data\, which a
re hardly asymptotic. Hence\, the assumption that an asymptotic extreme va
lue distribution is the appropriate distribution for the observed data is
based more or less on faith or convenience. Fortunately\, we now have reco
urse to the so-called ACER method\, which will serve as a complimentary to
olbox to the asymptotic approach. The ACER method can provide us with a no
nparametric replica of the extreme value distribution inherent in the data
. This allows us to use the obtained information as a diagnostic tool for
investigating the basis for e.g. applying asymptotic distributions. The AC
ER approach may also be used instead of the asymptotic distributions for e
xtreme value prediction. The ACER approach can also be used on nonstationa
ry time series\, which is essential for long term analyses. The methods wi
ll be discussed in detail and illustrated by several examples.\n\n\nCourse
material: Course slides and computer program with user’s guide.\n\n \n
\nShort biography of Professor Arvid Naess\n\n\nArvid Naess has been a Pro
fessor of Structural Engineering since 1987 and a Professor of Statistics
since 2001 at the Norwegian University of Science and Technology. He works
on a wide range of problems related to stochastic dynamics of structures
and structural safety and reliability\, where extreme value statistics is
an important element. Professor Naess has published more than 250 scientif
ic papers and lectured at conferences and universities worldwide. He is an
associate editor of many international journals. He is a recipient of the
Alfred M Freudenthal medal from ASCE\, and is an elected Fellow of ASME\,
ASCE and SEI. He is also an elected member of The Royal Norwegian Society
of Sciences and Letters and The Norwegian Academy of Technical Sciences.\
n\nhttps://www.ntnu.edu/employees/arvid.naess\n\n\n\nhttps://conferences.e
nbis.org/event/24/
LOCATION:
URL:https://conferences.enbis.org/event/24/
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