Bayesians, Frequentists, and Big Data: Musings on Statistics in the 21st Century


Details
Registration Process: Please register for our event in 2 places.
On the Predictive Analytics World website - If you don't also register here , you will not be admitted!: https://www.eiseverywhere.com/PAWNY11?categoryid=74686 Here on Meetup (just a formality, make sure to use link above to officially register for the event) Our October meeting will be hosted by the Predictive Analytics World (PAW) conference and will be preceded by a PAW reception which our members are invited to attend.
NOTE: You do not have to be a paid registrant of the PAW conference to attend the NYCPA event.
We are excited to welcome John W. Emerson, PhD., Associate Professor of Statistics, Department of Statistics, Yale University, as speaker for this event.
Agenda:
5:30 pm: Arrive early to pick up your name tag and peruse the PAW Exhibit Hall.
6:10 pm: Predictive Analytics World conference reception
7:30 pm: NYCPA event begins: Opening remarks, presentation by event sponsor JMP
8:00 pm: Dr. Emerson's presentation begins
ABSTRACT:
This talk will touch upon topics in data analysis, statistics, and computing relating to modern massive data challenges. How do classical theories in statistical inference and asymptotics translate into statistical practice in the modern world? What role should complex Bayesian procedures and other cutting-edge methodologies have in the data analyst toolkit? Computationally, how can we manage the data deluge and how is statistical software evolving? What are the implications for the data analyst? What are the dangers posed by
addressing these very questions? I'll suggest possible answers to some of these questions, and hope to spur further debate by posing others.
Speaker Bio:
John W. Emerson ("Jay") is an Associate Professor of Statistics at Yale University. Jay teaches both graduate and undergraduate courses and often includes timely real-world problems and examples in his lectures, an intersection of teaching and research. For example, he collaborated with the Wall Street Journal in uncovering the infamous
stock option backdating scandal, and he demonstrated a design flaw in the new scoring system used for international figure skating competitions. He has worked on Bayesian change point analyses and created the "generalized pairs plot" for the R Statistical Programming Environment. He has worked towards a scalable solution for statistical computing with massive data, extending support for the management, analysis, and exploration of massive data sets in R. He continues to work on the Yale/Columbia Environmental Performance Index (EPI), which he presented at the 2010 World Economic Forum in Davos.
Sponsored by:
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About Our Host:
Predictive Analytics World (http://www.predictiveanalyticsworld.com/newyork/2011/) is the business-focused event for predictive analytics professionals, managers and commercial practitioners. This conference delivers case studies, expertise and resources to achieve:
Bigger wins: Strengthen the impact of predictive analytics deployment Broader capabilities: Establish new opportunities with predictive analytics site URL: http://www.predictiveanalyticsworld.com/newyork/2011/
Text Analytics World (https://www.meetup.com/NYC-Predictive-Analytics/events/18541291/Text%20Analytics%20World%20is%20the%20business-focused%20event%20for%20text%20analytics%20professionals,%20managers%20and%20commercial%20practitioners.%20This%20conference%20delivers%20case%20studies,%20expertise%20and%20resources%20to%20achieve:%20Big%20value:%20Leverage%20unstructured%20data%20for%20business%20impact%20New%20methods:%20Deploy%20the%20latest%20text%20analytics%20technology) is the business-focused event for text analytics professionals, managers and commercial practitioners. This conference delivers case studies, expertise and resources to achieve:
Big value: Leverage unstructured data for business impact New methods: Deploy the latest text analytics technology site URL: http://www.textanalyticsworld.com/newyork/2011

Bayesians, Frequentists, and Big Data: Musings on Statistics in the 21st Century