DIMACS/Statistics Workshop on Fusion Learning, BFF Inferences, and Statistical Foundations: Third BFF (Bayesian, Frequentist & Fiducial) Workshop

April 11 - 13, 2016
DIMACS Center, CoRE Building, Rutgers University

Organizers:
Harry Crane, Rutgers University
Lee Dicker, Rutgers University
Ying Hung, Rutgers University
John Kolassa, Rutgers University
Regina Liu (co-chair), Rutgers University, rliu at stat.rutgers.edu
William E. Strawderman, Rutgers University
Han Xiao, Rutgers University
Min-ge Xie (co-chair), Rutgers University, mxie at stat.rutgers.edu
Dan Yang, Rutgers University
Local Committee:
Chengrui Li, Rutgers University
Sifan Liu, Rutgers University
Jieli Shen, Rutgers University
Xinyu Sun, Rutgers University
Suzanne Thornton, Rutgers University
Yeli Zhan, Rutgers University
Presented under the auspices of the DIMACS Special Focus on Information Sharing and Dynamic Data Analysis with additional support from the Rutgers Department of Statistics and Biostatistics and the National Science Foundation under grant number DMS-1107012.

Workshop Program:

Monday, April 11, 2016

 8:20 -  9:00  Registration/Breakfast

 9:00 -  9:15  Welcome and Opening Remarks
               Regina Liu, Chair, Department of Statistics, Rutgers University

 9:15 - 10:55  Chair, Regina Liu, Rutgers University
	
 9:15 -  9:55  We R "BFF" (Best Friends Forever) on road to BFF (Bayesian, Frequentist, Fiducial) inferences
	       Min-ge Xie, Rutgers University

 9:55 - 10:25  Confidence distributions for change points and regime shifts
               Nils Hjort, University of Oslo

10:25 - 10:55  Confidence estimating functions
               Peter Song, University of Michigan

10:55 - 11:15  Break

11:15 - 12:45  Chair, Min-ge Xie, Rutgers University
	
11:15 - 11:45  What does "frequentist" mean?
	       Glen Shafer, Rutgers University

11:45 - 12:45  Keynote: Confidence densities, uninformative priors, and the bootstrap
               Brad Efron, Stanford University
	       Discussant: Cun-Hui Zhang, Rutgers University 

12:45 -  2:00  Lunch and Poster Session

 2:00 -  3:40  Chair, Rong Chen, Rutgers University
	
 2:00 -  2:10  DIMACS Director's Welcome
               Tami Carpenter, Acting Deputy Director of DIMACS
	
 2:10 -  2:40  Taking Bayesian inference seriously
               Andrew Gelman, Columbia University	

 2:40 -  3:10  A problem in forensic science? Whose prior, whose Bayes factor, and who are you kidding?
               Hari Iyer and Steven Lund, National Institute of Standards and Technology (NIST)	

 3:10 -  3:40  Rigorizing and extending the CoxJaynes derivation of probability: implications for statistical practice 
               David Draper, UC-Santa Cruz

 3:40 -  3:55  Break

 3:55 -  5:30  Chair, William Strawderman, Rutgers University
	
 3:55 -  4:55  Keynote: The use of rejection odds and rejection ratios in testing hypotheses
               Jim Berger, Duke University
	       Discussant: Dongchu Sun, University of Missouri

 5:00 -  5:30  Workshop Mixer/Poster Session

Tuesday, April 12, 2016

 8:20 -  8:45  Registration/Breakfast

 8:45 - 10:30  Chair, Dennis Cox, Rice University
	
 8:45 -  9:30  Let's believe belief functions: a paradigm for multi-resolution probabilistic inference
               Xiao-Li Meng, Harvard University
	
 9:30 - 10:00  On beliefs, validity, and the foundations of statistics
               Ryan Martin, University of Illinois-Chicago
	
10:00 - 10:30  New challenges in generalized fiducial inference 
               Jan Hannig, UNC-Chapel Hill

10:30 - 10:50  Break

10:50 - 12:20  Chair, John Kolassa, Rutgers University
	
10:50 - 11:20  On the missing F in BFF 
               Nozer Singpurwalla, The City University of Hong Kong
	
11:20 - 11:50  Credibility of confidence sets in nonstandard econometric problems  
               Ulrich Müller, Princeton University

11:50 - 12:20  A sequential split-conquer-combine approach for analysis of big spatial data using confidence distributions 
               Ying Hung, Rutgers University

12:20 -  1:40  Lunch and Poster Session

 1:40 -  3:25  Chair, Dan Yang, Rutgers University
	
 1:40 -  2:25  What can we expect from distributions for parameters 
               Don Fraser, University of Toronto
	
 2:25 -  2:55  Sparse Simultaneous Signal Detection and Its Applications in Genomics  
               Hongzhe Li, University of Pennsylvania
	
 2:55 -  3:25  The backward reliability curve and its practical usefulness  
               Mounir Mesbah, Universite Pierre et Marie Curie, Paris 6

 3:25 -  3:40  Break

 3:40 -  5:20  Chair, Han Xiao, Rutgers University
	
 3:40 -  4:05  Combining one-sample confidence interval procedures for valid non-asymptotic inference in the two-sample case 
               Michael Fay, National Institute of Allergy and Infectious Diseases
	
 4:05 -  4:30  Fusion Learning: combining of inferences from diverse sources using data depth and confidence distribution 
               Dungang Liu
	
 4:30 -  4:55  Can big data help us better understand statistical foundations? 
               Keli Liu, Stanford University
	
 4:55 -  5:20  Edge exchangeability: a new foundation for modeling network data 
               Harry Crane, Rutgers University

 5:25 -  6:00  Poster session

Wednesday, April 13, 2016

 8:20 -  8:50  Registration/Breakfast

 8:50 - 10:35  Chair, Lee Dicker, Rutgers University
	
 8:50 -  9:35  Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity 
               Ed George, University of Pennsylvania

 9:35 - 10:05  Bayesian inference using Bregman divergence measures 
               Dipak Dey, University of Connecticut
	
10:05 - 10:35  The Spike-and-Slab LASSO
               Veronika Rockova, University of Pennsylvania

10:35 - 10:55  Break

10:55 - 12:25  Chair, Xiao-Li Meng, Harvard University
	
10:55 - 11:25  Still researching on asymptotic methods? Try generalized inference! 
               Sam Weeranhandi, Pfizer
	
11:25 - 12:25  Featured video talk: Data-based distributions for unknown parameters: always, sometimes, never? 
               Sir David R. Cox, Oxford University	
               Discussant: Nancy Reid, University of Toronto
 
12:25 -  1:30  Lunch

 1:30 -  3:10  Chair, Regina Liu, Rutgers University
	
 1:30 -  1:55  Synthesizing information and making local conclusions: multivariate inference, multiple tests, and not so many assumptions 
               Arne Bathke, University of Salzburg
	
 1:55 -  2:20  A new test for functional one-way ANOVA with application to ischemic heart screening  
               Ming-Yen Cheng, National Taiwan University
	
 2:20 -  2:45  The general univariate Dempster-Shafer model and its survival analysis counterpart for evaluating HIV-1 vaccine efficacy when censorship is not random 
               Paul Edlefsen, Fred Hutchinson Cancer Research Center
	
 2:45 -  3:10  Statistical inference theories, multiple uses of the same data, and past-realized data 
               Benjamin Holcblat and Steffen Grönneberg, BI Norwegian Business School

 3:10 -  3:20  Closing Remarks/Discussions



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Document last modified on April 11, 2016.