Dynamic precise and imprecise probability kinematics
M Caprio, R Gong. 2023. ISIPTA
Select publications and preprints (by topic)
For a complete list of publications, please refer to my CV.
On inference foundations
Six statistical senses
RV Craiu, R Gong, XL Meng. 2023. Annual Review of Statistics and Its Application
The value provided by a scientific explanation
R Gong, JB Kadane, MJ Schervish, T Seidenfeld, RB Stern. 2022. Reflections on the Foundations of Probability and Statistics: Essays in Honor of Teddy Seidenfeld
Learning and total evidence with imprecise probabilities
R Gong, JB Kadane, MJ Schervish, T Seidenfeld, RB Stern. 2022. International Journal of Approximate Reasoning
A Gibbs sampler for a class of random convex polytopes (with discussion)
PE Jacob, R Gong, PT Edlefsen, AP Dempster. 2021. Journal of the American Statistical Association
Judicious judgment meets unsettling updating: dilation, sure loss, and Simpson’s paradox (with discussion)
R Gong, XL Meng. 2021. Statistical Science
Deceptive credences
R Gong, JB Kadane, MJ Schervish, T Seidenfeld, RB Stern. 2021. Ergo, an Open Access Journal of Philosophy
Geometric conditions for the Discrepant Posterior Phenomenon and connections to Simpson’s paradox
Y Chen, R Gong, M Xie. 2020. ArXiv:2001.08336
Simultaneous inference under the vacuous orientation assumption
R Gong. 2019. ISIPTA
On statistical privacy
General inferential limits under differential and Pufferfish privacy
J Bailie, R Gong. 2024. International Journal of Approximate Reasoning
Differentially private range queries with correlated input perturbation
P Dharangutte, J Gao, R Gong, G Wang. 2024. ArXiv
Can swapping be differentially private? A refreshment stirred, not shaken
J Bailie, R Gong, XL Meng. 2023. NBER Working Paper
Integer subspace differential privacy
P Dharangutte, J Gao, R Gong, FY Yu. 2023. AAAI-23
Data augmentation MCMC for Bayesian inference from privatized data
N Ju, JA Awan, R Gong, VA Rao. 2022. NeurIPS
Exact inference with approximate computation for differentially private data via perturbations
R Gong. 2022. Journal of Privacy and Confidentiality
Transparent privacy is principled privacy
R Gong. 2022. Harvard Data Science Review
Subspace differential privacy
J Gao, R Gong, FY Yu. 2022. AAAI-22
Congenial differential privacy under mandated disclosure
R Gong, XL Meng. 2020. FODS-20
Data science and related topics
Informational richness and its impact on algorithmic fairness
M Di Bello, R Gong. 2023. Philosophical Studies
Technical Comment on ‘‘Policy impacts of statistical uncertainty and privacy’’
Y Cui, R Gong, J Hannig, K Hoffman. 2023. Science
David Kennard (Director): John Cleese’s Wine for the Confused
R Gong. 2022. Journal of Wine Economics
Discussion on prior-based Bayesian Information Criterion (PBIC) by MJ Bayarri, James O. Berger, Woncheol Jang, Surajit Ray, Luis R. Pericchi, and Ingmar Visser
R Gong, M Xie. 2019. Statistical Theory and Related Fields
A data-driven framework for identifying high school students at risk of not graduating on time
RA Johnson, R Gong, S Greatorex-Voith, A Anand, A Fritzler. 2015. Bloomberg Data for Good Exchange Conference
Contradictory conclusions in analysis of brain functional networks: the role of image registration
M Taquet, R Gong, SK Warfield. 2013. Organization for Human Brain Mapping (OHBM)