Seminars

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Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 28th July 2015
11:00am
Yanling (Tara) Cai, PhD Candidate in Statistics (UBC)
TBA
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TBA
Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Thu 16th July 2015
11:00am
Sean Jewell and Neil Spencer, Statistics Master's Students
TBA
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11am - 11:30am

Talk by Neil Spencer:  Title and abstract TBA.

11:30am - 12pm 

Talk by Sean Jewell:  Title and abstract TBA.


Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 14th July 2015
11:00am
TBA
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TBA
Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 7th July 2015
11:00am
Jinyuan Zhang, Statistics Master's Student
Conditional extremes in asymmetric financial markets
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The report focuses on the estimation of the probability distribution of a bivariate random vector given that one of the components takes on a large value. These conditional probabilities can be used to quantify the effect of financial contagion when the random vector represents losses on financial assets and as a stress-testing tool in financial risk management. In the context of risk management, the main interest lies in the tails of the underlying distribution. In such cases, empirical probabilities fail to provide adequate estimates while fully parametric methods are subject to large model uncertainty as there is too little data to assess the model fit in the tails. We propose a semi-parametric framework using asymptotic results in the spirit of extreme values theory. The main contributions include an extension of the limit theorem in {Abdous2005a} [Canad. J. Statist. 33 (2005)] to allow for asymmetry, frequently encountered in financial and insurance applications, and a new approach for inference.

Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Thu 2nd July 2015
11:00am
Sebastian Vollmer is a Departmental Lecturer with the statistics department, University of Oxford, since 2014 and was postdoc before in the groups of Arnaud Doucet and Yee Whye Teh. He has obtained is PhD in mathematics from the University of Warwick under the supervision of Professor Andrew Stuart and Professor Martin Hairer.
Stochastic gradient methods for estimating expectations
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Stochastic gradient methods have had great impact on tuning large scale models such as deep learning. This talk describes recent results of the use stochastic gradient methods for approximating expectation with respect to probability distributions. Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally expensive. Both the calculation of the acceptance probability and the creation of informed proposals usually require an iteration through the whole data set. The recently proposed stochastic gradient Langevin dynamics (SGLD) method circumvents this problem by generating proposals which are only based on a subset of the data, by skipping the accept-reject step. The talk surveys two recent preprints providing rigorous foundation on decreasing and non decreasing step size SGLD,
http://vollmer.ms/sebastian/sgld.pdf and http://vollmer.ms/sebastian/sgld2.pdf, respectively.

a place of mind, The University of British Columbia

Department of Statistics

Department of Statistics, University of British Columbia
3182 Earth Sciences Building
2207 Main Mall
Vancouver, BC, Canada V6T 1Z4
Tel: 604.822.0570
Fax: 604.822.6960

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