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Events - 2012

Thursday, April 12, 2012 — 4:00 PM EDT

Incorporating prior information to multiple hypothesis testing with applications to large-scale genetic studies

Friday, March 30, 2012 — 2:00 PM EDT

Emiliano A. Valdez, University of Connecticut

Thursday, March 22, 2012 — 4:00 PM EDT

Qihe Tang, University of Iowa

Thursday, March 15, 2012 — 4:00 PM EDT

Bruce L. Jones, University of Western Ontario

In establishing demographic assumptions for pension plan calculations, pension actuaries must decide on suitable termination rates. These rates typically depend on age and years of service, but may also depend on other factors such as economic conditions.

Monday, March 12, 2012 — 3:00 PM EDT

Yingli Qin, Nanyang Technological University, Singapore

Thursday, March 8, 2012 — 4:00 PM EST

Phelim Boyle, University of Waterloo

Thursday, March 1, 2012 — 4:00 PM EST

David Matthews, University of Waterloo

Tuesday, February 28, 2012 — 3:00 PM EST

Reza Ramezam, University of Waterloo

Transactional Analysis (TA), founded by Eric Berne in late 50s, is a psychological approach in studying interactions between individuals. This talk will provide a brief overview of how TA can be applied in teaching statistics courses to reduce statistics anxiety experienced by many students. It will be delivered in three sections:

Thursday, February 16, 2012 — 4:00 PM EST

Chad Q, University of North Carolina

Friday, February 10, 2012 — 10:30 AM EST

Ruodu Wang, Georga Institute of Technology

Wednesday, February 8, 2012 — 3:00 PM EST

Sphia Su, University of Minnesota

Friday, February 3, 2012 — 2:00 PM EST

KunLiang, University of Wisconsin-Madison

Tuesday, January 24, 2012 — 2:00 PM EST

Ioana Cosma, University of Cambridge

Thursday, January 19, 2012 — 4:00 PM EST

A reinsurance portfolio optimization approach for crop insurance

Tuesday, January 17, 2012 — 2:00 PM EST

Hedibert Lopes, University of Chicago

Wednesday, January 11, 2012 — 3:00 PM EST

Anindya Bhadra, Texas A&M University

Monday, January 9, 2012 — 2:00 PM EST

Robust and efficient analysis of conditionally-specified models via disparities

Disparity based inference proceeds by minimizing a measure of disparity between a parametric family of density functions and a kernel density estimate based on observed i.i.d. data. For a class of disparity measures, of which Hellinger distance is one of the best known, minimum disparity estimates of parameters have the surprising property of being both robust to outliers and also statistically efficient.

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