Ryan Martin : Research

Updated 03/29/2020

Research monographs


Lecture notes

These are notes based on the Stat 411 (Statistical Theory) and Stat 511/512 (Advanced Statistical Theory) courses that I taught several times while I was at the University of Illinois at Chicago, between 2011 and 2016. Both documents are technically still "works in progress" but they are readable and useable. Several instructors have asked if they can use these materials and I am happy to share them. If you're an instructor and would like to use these notes as a reference for your own course, that's great; but please contact me ahead of time, as I have a few very minor requests.

Working papers


Published papers

  1. Z. Wang and R. Martin (202x). Model-free posterior inference on the area under the receiver operating characteristic curve. Journal of Statistical Planning and Inference. [arXiv]

  2. C. Liu, Y. Yang, H. Bondell, and R. Martin (202x). Bayesian inference in high-dimensional linear models using an empirical correlation-adaptive prior. Statistica Sinica. [arXiv]

  3. L. Hong and R. Martin (202x). Discussion on "q-Credibility" by O. Le Courtois, Variance: Journal of the Casualty Actuarial Society. [ssrn]

  4. L. Hong and R. Martin (202x). Model misspecification, Bayesian versus credibility estimation, and Gibbs posteriors. Scandinavian Actuarial Journal. [researchers.one] [ssrn]

  5. R. Martin and B. Ning (202x). Empirical priors and coverage of posterior credible sets in a sparse normal mean model. Sankhya A. Special issue in memory of Professor J.K. Ghosh. [arXiv] [researchers.one]

  6. S. T. Tokdar and R. Martin (202x). Bayesian test of normality versus a Dirichlet process mixture alternative. Sankhya B. Special issue in memory of Professor J.K. Ghosh. [arXiv]

  7. R. Martin (202x). A survey of nonparametric mixing density estimation via the predictive recursion algorithm. Sankhya B. Special issue in memory of Professor J.K. Ghosh. [arXiv] [researchers.one]

  8. N. Syring and R. Martin (202x). Robust and rate-optimal Gibbs posterior inference on the boundary of a noisy image. The Annals of Statistics. [arXiv] [R code]

  9. Y. Lin, R. Martin, and M. Yang (2019). On optimal designs for non-regular models. The Annals of Statistics. [arXiv]

  10. R. Martin (2019). Empirical priors and posterior concentration rates for a monotone density. Sankhya A. [arXiv] [researchers.one] [R code]

  11. H. Crane and R. Martin (2019). Rethinking probabilistic prediction: lessons learned from the 2016 U.S. presidential election. Researchers.One.

  12. R. Martin and S. G. Walker (2019). Data-driven priors and their posterior concentration rates. Electronic Journal of Statistics. [arXiv]

  13. R. Martin (2019). False confidence, non-additive beliefs, and valid statistical inference. International Journal of Approximate Reasoning. Special issue for papers presented at the BELIEF 2018 conference in Compiegne, France. [arXiv] [researchers.one]

  14. M. Balch, R. Martin, and S. Ferson (2019). Satellite conjunction analysis and the false confidence theorem. Proceedings of the Royal Society, Series A. [arXiv]

  15. N. Syring, L. Hong, and R. Martin (2019). Gibbs posterior inference on value-at-risk. Scandinavian Actuarial Journal. [researchers.one]

  16. R. Martin (2019). Discussion of "Nonparametric generalized fiducial inference for survival functions under censoring". Biometrika.

  17. L. Cella and R. Martin (2019). Incorporating expert opinion in an inferential model while retaining validity. In Proceedings of Machine Learning Research (2019 International Symposium on Imprecise Probabilities: Theory & Applications).

  18. J. Cahoon and R. Martin (2019). Possibility measures for valid statistical inference based on censored data. In Proceedings of Machine Learning Research (2019 International Symposium on Imprecise Probabilities: Theory & Applications).

  19. R. Martin and N. Syring (2019). Validity-preservation properties of rules for combining inferential models. In Proceedings of Machine Learning Research (2019 International Symposium on Imprecise Probabilities: Theory & Applications). [researchers.one]

  20. R. Martin (2019). On valid uncertainty quantification about a model. In Proceedings of Machine Learning Research (2019 International Symposium on Imprecise Probabilities: Theory & Applications). [researchers.one]

  21. M. Chae, R. Martin, and S. G. Walker (2019). On an algorithm for solving Fredholm equations of the first kind. Statistics and Computing. [arXiv]

  22. N. Syring and R. Martin (2019). Calibrating general posterior credible regions. Biometrika. [arXiv] [R code]

  23. L. Hong and R. Martin (2019). Real-time Bayesian nonparametric prediction of solvency risk. Annals of Actuarial Science. [ssrn] [R code]

  24. H. Crane and R. Martin (2018). Academia's case of Stockholm syndrome. Quillette.

  25. H. Crane and R. Martin (2018). The researchers.one mission. Researchers.One.

  26. L. Hong, T. Kuffner, and R. Martin (2018). On prediction of future insurance claims when the model is uncertain. Variance: Journal of the Casualty Actuarial Society. [ssrn] [R code]

  27. P. R. Hahn, R. Martin, and S. G. Walker (2018). On recursive Bayesian predictive distributions. Journal of the American Statistical Association. [arXiv]

  28. M. Chae, R. Martin, and S. G. Walker (2018). Convergence of an iterative algorithm to the nonparametric MLE of a mixing distribution. Statistics & Probability Letters. [arXiv]

  29. L. Hong, T. Kuffner, and R. Martin (2018). On overfitting and post-selection uncertainty assessments. Biometrika. [arXiv]

  30. R. Martin, C. Ouyang, and F. Domagni (2018). 'Purposely misspecified' posterior inference on the volatility of a jump diffusion process. Statistics & Probability Letters. [arXiv]

  31. L. Hong and R. Martin (2018). Dirichlet process mixture models for insurance loss data. Scandinavian Actuarial Journal. [ssrn]

  32. R. Martin (2018). On an inferential model construction using generalized associations. Journal of Statistical Planning and Inference. Special issue on Confidence Distributions and Related Themes. [arXiv]

  33. R. Martin (2017). Invited comment on the article—"Uncertainty quantification for the horseshoe"—by van der Pas, Szabo, and van der Vaart. Bayesian Analysis.

  34. R. Martin, R. Mess, and S. G. Walker (2017). Empirical Bayes posterior concentration in sparse high-dimensional linear models. Bernoulli. [arXiv] [R code] (Some minor corrections are given in the arXiv version.)

  35. N. Syring and R. Martin (2017). Gibbs posterior inference on the minimum clinically important difference. Journal of Statistical Planning and Inference. [arXiv]

  36. R. Martin (2017). A statistical inference course based on p-values. The American Statistician. [arxiv]

  37. R. Martin (2017). Inferential models. Wiley StatsRef: Statistics Reference Online.

  38. L. Hong and R. Martin (2017). A review of Bayesian asymptotics in general insurance applications. European Actuarial Journal. [ssrn]

  39. L. Hong and R. Martin (2017). A flexible Bayesian nonparametric model for predicting future insurance claims. North American Actuarial Journal. [ssrn] [R code]

  40. C. Liu, R. Martin, and N. Syring (2017). Efficient simulation from a gamma distribution with small shape parameter. Computational Statistics. [arXiv] [R code]

  41. R. Martin (2017). Prior-free probabilistic inference for econometricians. In Robustness in Econometrics, Kreinovich, Sriboonchitta, and Huynh, Eds.

  42. R. Martin, J. Stufken, and M. Yang (2016). A conversation with Samad Hedayat. Statistical Science.

  43. R. Martin and R. Lingham (2016). Prior-free probabilistic prediction of future observations. Technometrics. [arXiv] [R code]

  44. R. Martin and Y. Lin (2016). Exact prior-free probabilistic inference in a class of non-regular models. Stat. [arXiv]

  45. R. Martin and Z. Han (2016). A semiparametric scale-mixture regression model and predictive recursion maximum likelihood. Computational Statistics and Data Analysis. [arXiv] [R code]

  46. L. Hong and R. Martin (2016). Discussion on "Credibility estimation of distribution functions with applications to experience rating in general insurance". North American Actuarial Journal. [ssrn]

  47. R. Martin (2015). Plausibility functions and exact frequentist inference. Journal of the American Statistical Association. [arXiv]

  48. R. Martin and C. Liu (2015). Marginal inferential models: prior-free probabilistic inference on interest parameters. Journal of the American Statistical Association. [arXiv]

  49. R. Martin and C. Liu (2015). Conditional inferential models: combining information for prior-free probabilistic inference. Journal of the Royal Statistical Society–Series B. [arXiv]

  50. R. V. Ramamoorthi, K. Sriram, and R. Martin (2015). On posterior concentration in misspecified models. Bayesian Analysis. [arXiv]

  51. C. Liu and R. Martin (2015). Frameworks for prior-free posterior probabilistic inference. WIREs: Computational Statistics. [arXiv]

  52. R. Martin (2015). Asymptotically optimal nonparametric empirical Bayes via predictive recursion. Communications in Statistics–Theory & Methods. [arXiv]

  53. Q. Cheng, X. Gao, and R. Martin (2014). Exact prior-free probabilistic inference on the heritability coefficient in a linear mixed effect model. Electronic Journal of Statistics. [arXiv] [R code]

  54. R. Martin and S. G. Walker (2014). Asymptotically minimax empirical Bayes estimation of a sparse normal mean vector. Electronic Journal of Statistics. [arXiv] [R code]

  55. R. Martin (2014). Random sets and exact confidence regions. Sankhya A. [arXiv]

  56. R. Martin and C. Liu (2014). A note on p-values interpreted as plausibilities. Statistica Sinica. [arXiv]

  57. R. Martin and C. Liu (2014). Discussion: Foundations of statistical inference, revisited. Statistical Science. [arXiv]

  58. R. Martin and C. Liu (2013). Inferential models: A framework for prior-free posterior probabilistic inference. Journal of the American Statistical Association. [arXiv] [R code] (Small correction and some extensions in the arXiv version, also published in the journal here.)

  59. R. Martin (2013). An approximate Bayesian marginal likelihood approach for estimating finite mixtures. Communications in Statistics–Simulation & Computation. [arXiv] [R code]

  60. R. Martin and O. Tilak (2012). On ε-optimality of the pursuit learning algorithm. Journal of Applied Probability. [arXiv]

  61. R. Martin and S. T. Tokdar (2012). A nonparametric empirical Bayes framework for large-scale multiple testing. Biostatistics. [arXiv]

  62. R. Martin (2012). Convergence rate for predictive recursion estimation of finite mixtures. Statistics & Probability Letters. [arXiv]

  63. R. Martin and S. T. Tokdar (2011). Semiparametric inference in mixture models with predictive recursion marginal likelihood. Biometrika. [arXiv]

  64. Z. Zhang, H. Xu, R. Martin, and C. Liu (2011). Inferential models for linear regression. Pakistan Journal of Statistics and Operations Research. Special issue on "Variable Selection in Regression".

  65. O. Tilak, R. Martin, and S. Mukhopadhyay (2011). Decentralized indirect method for learning automata games. IEEE Transactions on Systems, Man, and Cybernetics–Part B.

  66. R. Martin, J. Zhang, and C. Liu (2010). Dempster–Shafer theory and statistical inference with weak beliefs. Statistical Science. [arXiv]

  67. R. Martin and S. T. Tokdar (2009). Asymptotic properties of predictive recursion: robustness and rate of convergence. Electronic Journal of Statistics.

  68. S. T. Tokdar, R. Martin, and J. K. Ghosh (2009). Consistency of a recursive estimate of mixing distributions. The Annals of Statistics. [arXiv]

  69. R. Martin and J. K. Ghosh (2008). Stochastic approximation and Newton's estimate of a mixing distribution. Statistical Science. [arXiv]

  70. J. K. Ghosh and R. Martin (2008). On two fast algorithms for estimating the mixing distribution in mixture models. In Frontiers in Applied and Computational Mathematics, D. Blackmore, A. Bose and P. Petropoulos, Eds.


Software


Some presentations