Published papers

2026+

No-prior Bayes reIMagined: probabilistic approximations of possibilistic inferential models. Statistical Science, with discussion. [arXiv]

Comments on: Empirical Bayes for data integration by Rognon-Vael & Rossell (with E. Hector). TEST.

An efficient Monte Carlo method for valid prior-free possibilistic statistical inference. Journal of the American Statistical Association. [arXiv] [researchers.one]

Thomas Augustin's contributions to imprecise probability and statistics (with C. Jansen, J. Rodemann, G. Schollmeyer, and others). International Journal of Approximate Reasoning. Special issue in honor of the late T. Augustin.

Generalized universal inference on risk minimizers (with N. Dey and J. Williams). Journal of the Royal Statistical Society, Series B. [arXiv]

Hypothesis testing for community structure in temporal networks using e-values (with J. Williams and E. Yanchenko). Network Science. [arXiv]

E-processes, predictive recursion, and objective empirical probability (with V. Dixit). ERCIM News.

Decision-making with possibilistic inferential models (with S. Prim and J. Williams). International Journal of Approximate Reasoning. [arXiv] (Journal-invited extended version of the conference paper.)

Regularized e-processes: anytime valid inference with knowledge-based efficiency gains. Bernoulli. [arXiv] [researchers.one]

Possibilistic inferential models: a review. Journal of the American Statistical Association. [arXiv] [researchers.one]

The typicality principle and its implications for statistics and data science (with Y. Jiang, C. Liu, and Z. Zhang). Journal of Data Science. [arXiv]

Multiple testing in generalized universal inference (with N. Dey and J. Williams). Statistics & Probability Letters. Special issue on e-values and multiple testing. [arXiv]

2025

Advances in Bayesian model selection consistency for high-dimensional generalized linear models (with J. Lee and M. Chae). The Annals of Statistics. [arXiv]

Decision-theoretic properties of possibilistic inferential models. Proceedings of the 14th International Symposium on Imprecise Probabilities: Theories and Applications.

Computationally efficient variational-like approximations of possibilistic inferential models (with L. Cella). International Journal of Approximate Reasoning. [arXiv] (Journal-invited extended version of the conference paper.)

Asymptotic efficiency of inferential models and a possibilistic Bernstein--von Mises theorem (with J. Williams). International Journal of Approximate Reasoning. [arXiv] (Journal-invited extended version of the conference paper.)

Anytime valid and asymptotically optimal inference driven by predictive recursion (with V. Dixit). Biometrika. [arXiv] [researchers.one]

Empirical priors and posterior concentration in a piecewise polynomial sequence model (with C. Liu and W. Shen). Statistica Sinica. [arXiv]

2024

A possibility-theoretic solution to Basu's Bayesian--frequentist via media. Sankhya A. Special issue in honor of Debabrata Basu's birth centenary. [researchers.one] [arXiv]

Variational approximations of possibilistic inferential models (with L. Cella). Proceedings of the 8th International Conference on Belief Functions. [researchers.one] [arXiv]

Large-sample theory for inferential models: a possibilistic Bernstein–von Mises theorem (with J. Williams). Proceedings of the 8th International Conference on Belief Functions. [researchers.one] [arXiv]

Which statistical hypotheses are afflicted with false confidence? Proceedings of the 8th International Conference on Belief Functions. [researchers.one] [arXiv]

Contribution to the discussion of "Safe Testing" by Grunwald, de Heide, and Koolen (with N. Dey and J. Williams). Journal of the Royal Statistical Society, Series B. [pdf]

Seconder of the vote of thanks to Grunwald, de Heide, and Koolen and contribution to the discussion of "Safe Testing." Journal of the Royal Statistical Society, Series B. [pdf]

Empirical Bayes inference in sparse high-dimensional generalized linear models (with Y. Tang). Electronic Journal of Statistics. [arXiv]

Turning the information-sharing dial: efficient inference from different data sources (with E. Hector). Electronic Journal of Statistics. [researchers.one] [arXiv]

Valid model-free spatial prediction (with H. Mao and B. Reich). Journal of the American Statistical Association. [researchers.one] [arXiv]

Inferential models and possibility measures (with C. Liu). Handbook of Bayesian, Fiducial, and Frequentist Inference. [researchers.one] [arXiv]

Contribution to the discussion of 'Estimating means of bounded random variables by betting' by Waudby-Smith and Ramdas. Journal of the Royal Statistical Society, Series B. [pdf]

2023

Possibility-theoretic statistical inference offers performance and probativeness assurances (with L. Cella). International Journal of Approximate Reasoning. [researchers.one] [arXiv] (Journal-invited extended version of the conference paper.)

Fiducial inference viewed through a possibility-theoretic inferential model lens. Proceedings of the 13th International Symposium on Imprecise Probabilities: Theories and Applications. [researchers.one] [arXiv]

A PRticle filter algorithm for nonparametric estimation of multivariate mixing distributions (with V. Dixit). Statistics and Computing. [arXiv]

Revisiting consistency of a recursive estimator of mixing distributions (with V. Dixit). Electronic Journal of Statistics. [arXiv]

Gibbs posterior concentration rates under sub-exponential type losses (with N. Syring). Bernoulli. [arXiv]

A comparison of learning rate selection methods in generalized Bayesian inference (with P.-S. Wu). Bayesian Analysis. [arXiv]

2022

Direct and approximately valid probabilistic inference on a class of statistical functionals (with L. Cella). International Journal of Approximate Reasoning. [researchers.one] [arXiv] (Journal-invited extended version of the conference paper.)

Valid inferential models for prediction in supervised learning problems (with L. Cella). International Journal of Approximate Reasoning. [researchers.one] [arXiv] (Journal-invited extended version of the conference paper.)

Valid inferential models offer performance and probativeness assurances (with L. Cella). Proceedings of the 7th International Conference on Belief Functions.

A practical strategy for valid partial prior-dependent possibilistic inference (with D. Hose and M. Hanss). Proceedings of the 7th International Conference on Belief Functions. [researchers.one]

Direct Gibbs posterior inference on risk minimizers: construction, concentration, and calibration (with N. Syring). Handbook of Statistics: Advancements in Bayesian Methods and Implementation. [arXiv]

Estimating a mixing distribution on the sphere using predictive recursion (with V. Dixit). Sankhya B. [researchers.one] [arXiv]

Validity, consonant plausibility measures, and conformal prediction (with L. Cella). International Journal of Approximate Reasoning. Special issue in honor of Glenn Shafer's 75th birthday. [researchers.one] [arXiv]

Imprecise credibility theory (with L. Hong). Annals of Actuarial Science. [ssrn]

Asymptotically optimal inference in sparse sequence models with a simple data-dependent measure. Researchers.One. [arXiv]

Gibbs posterior inference on multivariate quantiles (with I. Bhattacharya). Journal of Statistical Planning and Inference. [arXiv]

2021

Bayesian estimation of sparse precision matrices in the presence of Gaussian measurement errors (with S. Ghoshal and W. Shi). Electronic Journal of Statistics.

Approximately valid and model-free possibilistic inference (with L. Cella). Proceedings of the 6th International Conference on Belief Functions.

Towards a theory of valid inferential models with partial prior information. Proceedings of the 6th International Conference on Belief Functions. [researchers.one]

Generalized inferential models for censored data (with J. Cahoon). International Journal of Approximate Reasoning. [researchers.one] [arXiv] (Journal-invited extended version of the conference paper.)

Stochastic optimization for numerical evaluation of imprecise probabilities (with N. Syring). Proceedings of the 12th International Symposium on Imprecise Probabilities: Theories and Applications. [arXiv]

Valid inferential models for prediction in supervised learning problems (with L. Cella). Proceedings of the 12th International Symposium on Imprecise Probabilities: Theories and Applications.

Response to the comment: "Confidence in confidence distributions!" (with M. Balch and S. Ferson). Proceedings of the Royal Society, Series A. [researchers.one] (Response to the 2020 comment by Cunen, Hjort, and Schweder on our 2019 paper.)

Valid model-free prediction of future insurance claims (with L. Hong). North American Actuarial Journal. [researchers.one] [ssrn]

Bayesian inference in high-dimensional linear models using an empirical correlation-adaptive prior (with H. Bondell, C. Liu, and Y. Yang). Statistica Sinica. [arXiv]

Zero-knowledge data analysis to solve the "other" file drawer problem (with H. Crane and M. Stephenson). Researchers.One

A survey of nonparametric mixing density estimation via the predictive recursion algorithm. Sankhya B. Special issue in memory of Jayanta K. Ghosh. [arXiv] [researchers.one]

Bayesian test of normality versus a Dirichlet process mixture alternative (with S. Tokdar). Sankhya B. Special issue in memory of Jayanta K. Ghosh. [arXiv]

Comment on Shafer's "Testing by betting: A strategy for statistical and scientific communication." Journal of the Royal Statistical Society, Series A. [pdf]

Settle the unsettling (with C. Liu). Comment on Gong and Meng's "Judicious judgment meets unsettling updating: Dilation, sure loss, and Simpson's paradox." Statistical Science. [pdf]

2020

Empirical priors and coverage of posterior credible sets in a sparse normal mean model (with B. Ning). Sankhya A. Special issue in memory of Jayanta K. Ghosh. [arXiv] [researchers.one]

Generalized inferential models for meta-analyses based on few studies (with J. Cahoon). Statistics and Applications. Special issue in honor of Bikas and Bimal Sinha on their 75th birthday. [researchers.one] [arXiv]

Model misspecification, Bayesian versus credibility estimation, and Gibbs posteriors (with L. Hong). Scandinavian Actuarial Journal. [researchers.one] [ssrn]

Robust and rate-optimal Gibbs posterior inference on the boundary of a noisy image (with N. Syring). The Annals of Statistics. [arXiv] [R code]

Empirical priors for prediction in sparse high-dimensional linear regression (with Y. Tang). Journal of Machine Learning Research. [arXiv] [researchers.one] [R code]

Model-free posterior inference on the area under the receiver operating characteristic curve (with Z. Wang). Journal of Statistical Planning and Inference. [arXiv]

Comment on the proposal to rename the R. A. Fisher lecture (with H. Crane and J. Guinness). Researchers.One.

Variational approximations of empirical Bayes posteriors in high-dimensional linear models (with Y. Yang). [arXiv]

An empirical G-Wishart prior for sparse high-dimensional Gaussian graphical models (with C. Liu). [arXiv]

2019

False confidence, non-additive beliefs, and valid statistical inference. International Journal of Approximate Reasoning. [arXiv] [researchers.one]

Satellite conjunction analysis and the false confidence theorem (with M. Balch and S. Ferson). Proceedings of the Royal Society, Series A. [arXiv]

On optimal designs for non-regular models (with Y. Lin and M. Yang). The Annals of Statistics. [arXiv]

Empirical priors and posterior concentration rates for a monotone density. Sankhya A. [arXiv] [researchers.one] [R code]

Rethinking probabilistic prediction: lessons learned from the 2016 U.S. presidential election (with H. Crane). Researchers.One.

Data-driven priors and their posterior concentration rates (with S. Walker). Electronic Journal of Statistics. [arXiv]

Gibbs posterior inference on value-at-risk (with L. Hong and N. Syring). Scandinavian Actuarial Journal. [researchers.one]

Incorporating expert opinion in an inferential model while retaining validity (with L. Cella). Proceedings of the 11th International Symposium on Imprecise Probabilities: Theories and Applications.

Possibility measures for valid statistical inference based on censored data (with J. Cahoon). Proceedings of the 11th International Symposium on Imprecise Probabilities: Theories and Applications.

Validity-preservation properties of rules for combining inferential models (with N. Syring). Proceedings of the 11th International Symposium on Imprecise Probabilities: Theories and Applications. [researchers.one]

On valid uncertainty quantification about a model. Proceedings of the 11th International Symposium on Imprecise Probabilities: Theories and Applications. [researchers.one]

On an algorithm for solving Fredholm equations of the first kind (with M. Chae and S. Walker). Statistics and Computing. [arXiv]

Calibrating general posterior credible regions (with N. Syring). Biometrika. [arXiv] [R code]

Real-time Bayesian nonparametric prediction of solvency risk (with L. Hong). Annals of Actuarial Science. [ssrn] [R code]

Discussion of "Nonparametric generalized fiducial inference for survival functions under censoring", by Y. Cui and J. Hannig. Biometrika.

Ten hot topics around scholarly publishing (with J. P. Tennant and others). Publications.

What is the purpose of peer review? (with H. Crane). Heterodox Academy Blog.

Variational approximations using Fisher divergence (with H. Bondell and Y. Yang). [arXiv]

2018

On recursive Bayesian predictive distributions (with P. Hahn and S. Walker). Journal of the American Statistical Association. [arXiv]

On prediction of future insurance claims when the model is uncertain (with L. Hong and T. Kuffner). Variance: Journal of the Casualty Actuarial Society. [ssrn] [R code]

On an inferential model construction using generalized associations. Journal of Statistical Planning and Inference. [arXiv]

Convergence of an iterative algorithm to the nonparametric MLE of a mixing distribution (with M. Chae and S. Walker). Statistics & Probability Letters. [arXiv]

On overfitting and post-selection uncertainty assessments (with L. Hong and T. Kuffner). Biometrika. [arXiv]

'Purposely misspecified' posterior inference on the volatility of a jump diffusion process (with F. Domagni and C. Ouyang). Statistics & Probability Letters. [arXiv]

Dirichlet process mixture models for insurance loss data (with L. Hong). Scandinavian Actuarial Journal. [ssrn]

In peer review we (don't) trust: How peer review's filtering poses a systemic risk to science (with H. Crane). Researchers.One.

Is statistics meeting the needs of science? (with H. Crane). Researchers.One

Academia's case of Stockholm syndrome (with H. Crane). Quillette.

The Researchers.One mission (with H. Crane). Researchers.One.

2017

Empirical Bayes posterior concentration in sparse high-dimensional linear models (with R. Mess and S. Walker). Bernoulli. [arXiv] [R code] (Some minor corrections are given in the arXiv version.)

Gibbs posterior inference on the minimum clinically important difference (with N. Syring). Journal of Statistical Planning and Inference. [arXiv]

A statistical inference course based on p-values. The American Statistician. [arxiv]

Inferential models. Wiley StatsRef: Statistics Reference Online.

A review of Bayesian asymptotics in general insurance applications (with L. Hong). European Actuarial Journal. [ssrn]

A flexible Bayesian nonparametric model for predicting future insurance claims (with L. Hong). North American Actuarial Journal. [ssrn] [R code]

Efficient simulation from a gamma distribution with small shape parameter (with C. Liu and N. Syring). Computational Statistics. [arXiv] [R code]

Comment on van der Pas, Szabo, and van der Vaart's "Uncertainty quantification for the horseshoe". Bayesian Analysis.

Prior-free probabilistic inference for econometricians. In Robustness in Econometrics.

2016

Prior-free probabilistic prediction of future observations (with R. Lingham). Technometrics. [arXiv] [R code]

Exact prior-free probabilistic inference in a class of non-regular models (with Y. Lin). Stat. [arXiv]

A conversation with Samad Hedayat (with J. Stufken and M. Yang). Statistical Science.

A semiparametric scale-mixture regression model and predictive recursion maximum likelihood (with Z. Han). Computational Statistics and Data Analysis. [arXiv] [R code]

Discussion of Cai, Wen, Wu, and Zhou's "Credibility estimation of distribution functions with applications to experience rating in general insurance" (with L. Hong). North American Actuarial Journal. [ssrn]

Valid uncertainty quantification about the model in linear regression (with C. Liu, H. Xu, and Z. Zhang). [arXiv]

2015

Plausibility functions and exact frequentist inference. Journal of the American Statistical Association. [arXiv]

Marginal inferential models: prior-free probabilistic inference on interest parameters (with C. Liu). Journal of the American Statistical Association. [arXiv]

Conditional inferential models: combining information for prior-free probabilistic inference (with C. Liu). Journal of the Royal Statistical Society–Series B. [arXiv]

On posterior concentration in misspecified models (with R. V. Ramamoorthi and K. Sriram). Bayesian Analysis. [arXiv]

Frameworks for prior-free posterior probabilistic inference (with C. Liu). WIREs: Computational Statistics. [arXiv]

Asymptotically optimal nonparametric empirical Bayes via predictive recursion. Communications in Statistics–Theory & Methods. [arXiv]

2014

Exact prior-free probabilistic inference on the heritability coefficient in a linear mixed effect model (with Q. Cheng and X. Gao). Electronic Journal of Statistics. [arXiv] [R code]

Asymptotically minimax empirical Bayes estimation of a sparse normal mean vector (with S. Walker). Electronic Journal of Statistics. [arXiv] [R code]

Random sets and exact confidence regions. Sankhya A. [arXiv]

A note on p-values interpreted as plausibilities (with C. Liu). Statistica Sinica. [arXiv]

Foundations of statistics, revisited (with C. Liu). Comment on Mayo's "On the Birnbaum argument for the strong likelihood principle". Statistical Science. [arXiv]

2013

Inferential models: A framework for prior-free posterior probabilistic inference (with C. Liu). Journal of the American Statistical Association. [arXiv] [R code] (Small correction and some extensions in the arXiv version, also published in the journal here.)

An approximate Bayesian marginal likelihood approach for estimating finite mixtures. Communications in Statistics–Simulation & Computation. [arXiv] [R code]

A note on Bayesian convergence rates under local prior support conditions (with L. Hong and S. Walker). [arXiv]

Optimal inferential models for a Poisson mean (with D. Ermini Leaf and C. Liu). [arXiv] [R code]

2012

On ε-optimality of the pursuit learning algorithm (with O. Tilak). Journal of Applied Probability. [arXiv]

A nonparametric empirical Bayes framework for large-scale multiple testing (with S. Tokdar). Biostatistics. [arXiv]

Convergence rate for predictive recursion estimation of finite mixtures. Statistics & Probability Letters. [arXiv]

On convergence rates of Bayesian predictive densities and posterior distributions (with L. Hong). [arXiv]

2011

Semiparametric inference in mixture models with predictive recursion marginal likelihood (with S. Tokdar). Biometrika. [arXiv]

Inferential models for linear regression (with C. Liu, H. Xu, and Z. Zhang). Pakistan Journal of Statistics and Operations Research.

Decentralized indirect method for learning automata games (with S. Mukhopadhyay and O. Tilak). IEEE Transactions on Systems, Man, and Cybernetics, Series B.

2010

Dempster–Shafer theory and statistical inference with weak beliefs (with C. Liu and J. Zhang). Statistical Science. [arXiv]

2009

Fast Nonparametric Estimation of Mixing Distributions with Application to High-Dimensional Inference. Ph.D. thesis [pdf]

Asymptotic properties of predictive recursion: robustness and rate of convergence (with S. Tokdar). Electronic Journal of Statistics.

Consistency of a recursive estimate of mixing distributions (with J. K. Ghosh and S. Tokdar). The Annals of Statistics. [arXiv]

2008

Stochastic approximation and Newton's estimate of a mixing distribution (with J. K. Ghosh). Statistical Science. [arXiv]

On two fast algorithms for estimating the mixing distribution in mixture models (with J. K. Ghosh). In Frontiers in Applied and Computational Mathematics.