[Statlist] Next talk: Friday, March 16, 2012 - 15.15h - with Sander Greenland, University of California, Los Angeles

[Cecilia Rey]

Kindly note that the talk with Sander Greenland stated below of

Friday, March 16, 2012, takes place at 15.15h (and not 16.15h) as  
mentioned in yesterday's announcement.

Best regards,
Cecilia Rey
-- 
ETH Z��rich
Seminar f��r Statistik
Cecilia Rey-Lutz, HG G10.3
R��mistrasse 101
CH-8092 Zurich		                      	
mail: rey using stat.math.ethz.ch    	  		
phone: +41 44 632 3438/fax: +41 44 632 1228


> Von: Cecilia Rey <rey using stat.math.ethz.ch>
> Datum: 12. M��rz 2012 15:10:14 GMT+01:00
> An: Torsten Hothorn <Torsten.Hothorn using stat.uni-muenchen.de>, <statlist using stat.math.ethz.ch 
> >
> Kopie: Cecilia Rey <rey using stat.math.ethz.ch>
> Betreff: Next talk: Friday, March 16, 2012 with Sander Greenland,  
> University of California, Los Angeles
>
> ETH and University of Zurich
>
> Proff. P. Buehlmann -  H.R. Kuensch -
> M. Maathuis -  S. van de Geer - M. Wolf
>
>
> *******************************************************************************
> We are glad to announce the following talk
>
> Friday, March 16, 2012, 15.15h, HG G 19.1
>
> ******************************************************************************
>
>  by Sander Greenland, University of California, Los Angeles
>
> Title:
> Integrating Bayesian and frequentist statistics, or: Seeing both  
> sides of the same biased coin.
>
> Absract:
> Outlines of a bayes���non���Bayes compromise or fusion have been  
> emerging for decades.
> Nonetheless, basic teaching remains mired in conventional  
> frequentist methods that are
> misunderstood and misrepresented by most users (including many  
> statisticians) and that are
> highly misleading outside of ideal experimental conditions. Thus it  
> is essential to revolutionize
> how we introduce elementary statistical inference in health and  
> social science, by providing
> Bayesian concepts and methods in tandem with frequentist concepts  
> and methods. Contrary to
> prevalent beliefs, basic Bayesian methods require no new  
> computational formulas or software
> beyond familiar frequentist ones; they do not even require Bayes���  
> theorem. Those methods can
> help reveal untenably strong assumptions hidden in conventional  
> methods, and allow relaxation of
> those assumptions into a more reasonable form.
>
> Background cite: Greenland, S. (2009). Relaxation penalties and  
> priors for plausible modeling of
> nonidentified bias sources. Statistical Science, 24, 195���210
>
> ___________________________________________________________________________
>
> The abstract is also to be found here:  http://stat.ethz.ch/events/research_seminar
>


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