Fusion Learning & BFF
(Bayesian, frequentist and fiducial) Inferences and Statistical Foundations
Department of Statistics & DIMACS
Rutgers University
April 11-13, 2016
CoRE Auditorium, Busch Campus, Rutgers University
Program
Monday, April 11
8:20am-9:00am: Registration/Breakfast
9:00am-9:15am: Welcome and Opening Remarks
Regina Liu, Chair, Department of Statistics, Rutgers University
9:15am-10:55am: Chair, Regina Liu, Rutgers University
· 9:15am-9:55am: Min-ge Xie, Rutgers University
We R "BFF" (Best Friend Forever) on Road to BFF (Bayesian, Frequentist, Fiducial) Inferences
· 9:55am-10:25am: Nils Hjort, University of Oslo
Confidence distributions for change points and regime shifts
· 10:25am-10:55am: Peter Song, University of Michigan
Confidence estimating functions
(10:55am-11:15am: Break)
11:15am-12:45pm: Chair, Min-ge Xie, Rutgers University
· 11:15am-11:45am: Glenn Shafer, Rutgers University
What does “frequentist” mean?
· 11:45am-12:45pm (Keynote): Brad Efron, Stanford University
Confidence densities, uninformative priors, and the bootstrap
· Discussant: Cun-Hui Zhang, Rutgers University
(12:45pm-2:00pm: Lunch and Poster Session)
2:00pm-3:40pm: Chair, Rong Chen, Rutgers University
· 2:00pm-2:10pm: DIMACS Director’s Welcome, Rebecca Wright, Director of DIMACS
· 2:10pm-2:40pm: Andrew Gelman, Columbia University
Taking Bayesian inference seriously
· 2:40pm-3:10pm: Hari Iyer and Steven Lund, National Institute of Standards and Technology (NIST)
A problem in forensic science? Whose prior, whose Bayes factor, and who are you kidding?
· 3:10pm-3:40pm: David Draper, UC-Santa Cruz
Rigorizing and extending the Cox–Jaynes derivation of probability: implications for statistical practice
(3:40pm-3:55pm: Break)
3:55pm-5:30pm: Chair, William Strawderman, Rutgers University
· 3:55pm-4:55pm (Keynote): Jim Berger, Duke University
The use of rejection odds and rejection ratios in testing hypotheses
· Discussant: Dongchu Sun, University of Missouri
5:00pm-5:30pm: Workshop Mixer/Poster Session
Tuesday, April 12
8:20am-8:45am: Registration/Breakfast
8:45am-10:30am: Chair, Dennis Cox, Rice University
· 8:45am-9:30am: Xiao-Li Meng, Harvard University
Let’s believe belief functions: a paradigm for multi-resolution probabilistic inference
· 9:30am-10:00am: Ryan Martin, University of Illinois-Chicago
On beliefs, validity, and the foundations of statistics
· 10:00am-10:30am: Jan Hannig, UNC-Chapel Hill
New challenges in generalized fiducial inference
(10:30am-10:50am: Break)
10:50am-12:20pm: Chair, John Kolassa, Rutgers University
· 10:50am-11:20am: Nozer Singpurwalla, The City University of Hong Kong
On the missing F in BFF
· 11:20am-11:50am: Ulrich Müller, Princeton University
Credibility of confidence sets in nonstandard econometric problems
· 11:50am-12:20pm: Ying Hung, Rutgers University
A sequential split-conquer-combine approach for analysis of big spatial data using confidence distributions
(12:20pm-1:40pm: Lunch and Poster Session)
1:40pm-3:25pm: Chair, Dan Yang, Rutgers University
· 1:40pm-2:25pm: Don Fraser, University of Toronto
What can we expect from distributions for parameters
· 2:25pm-2:55pm: Hongzhe Li, University of Pennsylvania
Sparse Simultaneous Signal Detection and Its Applications in Genomics
· 2:55pm-3:25pm: Mounir Mesbah, Université Pierre et Marie Curie, Paris 6
The backward reliability curve and its practical usefulness
(3:25pm-3:40pm: Break)
3:40pm-5:20pm: Chair, Han Xiao, Rutgers University
· 3:40pm-4:05pm: Michael Fay, National Institute of Allergy and Infectious Diseases
Combining one-sample confidence interval procedures for valid non-asymptotic inference in the two-sample case
· 4:05pm-4:30pm: Dungang Liu
Fusion Learning: combining of inferences from diverse sources using data depth and confidence distribution
· 4:30pm-4:55pm: Keli Liu, Stanford University
Can big data help us better understand statistical foundations?
· 4:55pm-5:20pm: Harry Crane, Rutgers University
Edge exchangeability: a new foundation for modeling network data
5:25pm-6:00pm: Poster session
Wednesday, April 13
8:20am-8:50am: Registration/Breakfast
8:50am-10:35am: Chair, Lee Dicker, Rutgers University
· 8:50am-9:35am: Ed George, University of Pennsylvania
Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity
· 9:35am-10:05am: Dipak Dey, University of Connecticut
Bayesian inference using Bregman divergence measures
· 10:05am-10:35am: Veronika Rockova, University of Pennsylvania
The Spike-and-Slab LASSO
(10:35am-10:55am: Break)
10:55am-12:25pm: Chair, Xiao-Li Meng, Harvard University
· 10:55am-11:25am: Sam Weeranhandi, Pfizer
Still researching on asymptotic methods? Try generalized inference!
· 11:25am-12:25pm (Featured video talk): Sir David R. Cox, Oxford University
Data-based distributions for unknown parameters: always, sometimes, never?
· Discussant: Nancy Reid, University of Toronto
(12:25pm-1:30pm: Lunch)
1:30pm-3:10pm: Chair, Regina Liu, Rutgers University
· 1:30pm-1:55pm: Arne Bathke, University of Salzburg
Synthesizing information and making local conclusions: multivariate inference, multiple tests, and not so many assumptions
· 1:55pm-2:20pm: Ming-Yen Cheng, National Taiwan University
A new test for functional one-way ANOVA with application to ischemic heart screening
· 2:20pm-2:45pm: Paul Edlefsen, Fred Hutchinson Cancer Research Center
The general univariate Dempster-Shafer model and its survival analysis counterpart for evaluating HIV-1 vaccine efficacy when censorship is not random
· 2:45pm-3:10pm: Benjamin Holcblat and Steffen Grønneberg, BI Norwegian Business School
Statistical inference theories, multiple uses of the same data, and past-realized data
3:10pm-3:20pm: Closing Remarks/Discussions
Organizing Committee:
Harry Crane, Lee Dicker, Ying Hung, Regina Liu (co-chair), John Kolassa, William Strawderman, Han Xiao, Minge Xie (co-chair), Dan Yang
For more information: http://www.stat.rutgers.edu
