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METHOD:PUBLISH
X-WR-CALNAME:Centre for Mathematical Sciences
X-ORIGINAL-URL:https://math-sciences.org
X-WR-CALDESC:Events for Centre for Mathematical Sciences
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TZID:UTC
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TZOFFSETFROM:+0000
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DTSTART:20180101T000000
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DTSTART;TZID=UTC:20180613T140000
DTEND;TZID=UTC:20180613T150000
DTSTAMP:20230401T132731
CREATED:20180105T174103Z
LAST-MODIFIED:20180504T150256Z
UID:2716-1528898400-1528902000@math-sciences.org
SUMMARY:Keming Yu (Brunel University)
DESCRIPTION:Title: Probabilistic Regression Analysis of Extreme Events in Energy Sectors with Either Massive or Small Data \nAbstract:\nBuried pipelines and wind turbine are important devices to convert or transfer energy. Buried pipelines are vulnerable to the threat of corrosion. Of interest is an estimate of the probability when or where an affected pipeline is likely to fail from the extreme growth of a corrosion defect. Wind turbine monitoring uses acoustic emission signal detection of damage processes in the structure. Peak signals are something under the concern of the industry. Of interest is an estimate of the probability of a signal beyond a threshold. Many factors involved need to be taken into consideration when building a probabilistic model for these extreme events. But some classical regression models such as the logistic regression whose response is a binary variable seems inefﬁcient for an observable continuous-response. Furthermore\, the probit regression may face either massive data to process or small size to apply\, depending on different cases. Whatever the case\, estimation accuracy with the support of sound statistical theory and computational algorithm is expected. This talk will introduce a novel inference of probit regression for extreme events to cope with either massive streaming or small size data and show that this objective may be achievable.
URL:https://math-sciences.org/event/keming-yu-brunel-university/
LOCATION:Rolle 206
CATEGORIES:Seminars,Statistics and Data Science
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