Close

Not a member yet? Register now and get started.

lock and key

Sign in to your account.

Account Login

Forgot your password?

Dr. Axtell Friday Colloquium speaker

News and Events | Comments Off on Dr. Axtell Friday Colloquium speaker

The Research Colloquium on Computational Social Science/Data Sciences speaker for Friday, November 01, 2019, will be Robert Axtell, Professor, Department of Computational and Data Sciences. Dr. Axtell’s talk entitled “Working with Heavy-Tailed Data: A Tutorial” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall.  The talk will be followed by a Q&A session along with light refreshments. 

This session will be live-streamed on the YouTube channel:  https://www.youtube.com/channel/UC7YCR-pBTZ_9865orDNVHNA

For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/

 For a list of upcoming and previous seminars, please visit:  https://cos.gmu.edu/cds/calendar/

 

Abstract:  This will be a hands-on talk in which Dr. Axtell will illustrate challenges and pitfalls of manipulating and statistically describing data which are extremely skew and possibly large in quantity. Motivated mainly by models from economics (e.g., firms and cities) and finance, datasets with millions of observations, both continuous and discrete, will be analyzed and plotted, with and without binning. There will be some discussion of parameter estimation but this will not be the primary focus of the talk. Heavy-tailed size distributions, the corresponding weighted-distributions, and the related ideas of moment distributions and size-biased sampling will all be discussed. Terminology such as ‘scaling’ and ‘scale-free’ will be unpacked and illustrated. The relation of Zipf-style rank-size plots to probability distributions will be developed. The competition between power law and lognormal distributions to represent heavy-tailed data will be addressed, including the ability of the lognormal to mimic a power law. Finite size effects and truncated distributions will also be discussed.