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

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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