Ryan Martin : Research

Updated 02/28/2018

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, then that's great, but please contact me to let me know your intentions.

Technical reports

Superscript "s" indicates a co-author who was/is a student advisee.

Published/Accepted papers

Superscript "s" indicates a co-author who was/is a student advisee.
  1. L. Hong, T. Kuffner, and R. Martin (2018). On overfitting and post-selection uncertainty assessments. Biometrika, volume 105, pages 221–224. [arXiv]

  2. L. Hong and R. Martin (201x). Real-time Bayesian nonparametric prediction of solvency risk. Annals of Actuarial Science, to appear. [ssrn] [R code]

  3. P. R. Hahn, R. Martin, and S. G. Walker (201x). On recursive Bayesian predictive distributions. Journal of the American Statistical Association, to appear. [arXiv]

  4. R. Martin, C. Ouyang, and F. Domagnis (2018). 'Purposely misspecified' posterior inference on the volatility of a jump diffusion process. Statistics & Probability Letters, volume 134, pages 106–113. [arXiv]

  5. L. Hong and R. Martin (201x). Dirichlet process mixture models for insurance loss data. Scandinavian Actuarial Journal, to appear. [ssrn]

  6. R. Martin (2018). On an inferential model construction using generalized associations. Journal of Statistical Planning and Inference, volume 195, pages 105–115; special issue on Confidence Distributions and Related Themes. [arXiv]

  7. R. Martin (2017). Comment on the article—"Uncertainty quantification for the horseshoe"—by van der Pas, Szabo, and van der Vaart. Bayesian Analysis, volume 12, pages 1254–1258. [isba news]

  8. R. Martin, R. Messs, and S. G. Walker (2017). Empirical Bayes posterior concentration in sparse high-dimensional linear models. Bernoulli, volume 23, pages 1822–1847. [arXiv] [R code] (Some minor corrections are given in the arXiv version.)

  9. N. Syrings and R. Martin (2017). Gibbs posterior inference on the minimum clinically important difference. Journal of Statistical Planning and Inference, volume 187, pages 67–77. [arXiv]

  10. R. Martin (2017). A statistical inference course based on p-values. The American Statistician, volume 71, pages 128–136. [arxiv]

  11. R. Martin (2017). Inferential models. Wiley StatsRef: Statistics Reference Online, pages 1–8.

  12. L. Hong and R. Martin (2017). A review of Bayesian asymptotics in general insurance applications. European Actuarial Journal, volume 7, pages 231–255. [ssrn]

  13. L. Hong and R. Martin (2017). A flexible Bayesian nonparametric model for predicting future insurance claims. North American Actuarial Journal, volume 21, pages 228–241. [ssrn] [R code]

  14. C. Liu, R. Martin, and N. Syrings (2017). Efficient simulation from a gamma distribution with small shape parameter. Computational Statistics, volume 32, pages 1767–1775. [arXiv] [R code]

  15. R. Martin (2017). Prior-free probabilistic inference for econometricians. In Robustness in Econometrics, Kreinovich, Sriboonchitta, and Huynh, Eds. Springer International, Studies in Computational Intelligence, volume 692, pages 169–186.

  16. R. Martin, J. Stufken, and M. Yang (2016). A conversation with Samad Hedayat. Statistical Science, volume 31, pages 637–647.

  17. R. Martin and R. Lingham (2016). Prior-free probabilistic prediction of future observations. Technometrics, volume 58, pages 225–235. [arXiv] [R code]

  18. R. Martin and Y. Lins (2016). Exact prior-free probabilistic inference in a class of non-regular models. Stat, volume 5, pages 312–321. [arXiv]

  19. R. Martin and Z. Hans (2016). A semiparametric scale-mixture regression model and predictive recursion maximum likelihood. Computational Statistics and Data Analysis, volume 94, pages 75–85. [arXiv] [R code]

  20. 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, volume 20, pages 95–98. [ssrn]

  21. R. Martin (2015). Plausibility functions and exact frequentist inference. Journal of the American Statistical Association, volume 110, pages 1552–1561. [arXiv]

  22. R. Martin and C. Liu (2015). Marginal inferential models: prior-free probabilistic inference on interest parameters. Journal of the American Statistical Association, volume 110, pages 1621–1631. [arXiv]

  23. R. Martin and C. Liu (2015). Conditional inferential models: combining information for prior-free probabilistic inference. Journal of the Royal Statistical Society–Series B, volume 77, pages 195–217. [arXiv]

  24. R. V. Ramamoorthi, K. Sriram, and R. Martin (2015). On posterior concentration in misspecified models. Bayesian Analysis, volume 10, pages 759–789. [arXiv]

  25. C. Liu and R. Martin (2015). Frameworks for prior-free posterior probabilistic inference. WIREs: Computational Statistics, volume 7, pages 77–85; invited review paper. [arXiv]

  26. R. Martin (2015). Asymptotically optimal nonparametric empirical Bayes via predictive recursion. Communications in Statistics–Theory & Methods, volume 44, pages 286–299. [arXiv]

  27. Q. Chengs, X. Gaos, and R. Martin (2014). Exact prior-free probabilistic inference on the heritability coefficient in a linear mixed effect model. Electronic Journal of Statistics, volume 8, pages 3062–3076. [arXiv] [R code]

  28. R. Martin and S. G. Walker (2014). Asymptotically minimax empirical Bayes estimation of a sparse normal mean vector. Electronic Journal of Statistics, volume 8, pages 2188–2206. [arXiv] [R code]

  29. R. Martin (2014). Random sets and exact confidence regions. Sankhya A, volume 76, pages 288–304. [arXiv]

  30. R. Martin and C. Liu (2014). A note on p-values interpreted as plausibilities. Statistica Sinica, volume 24, pages 1703–1716. [arXiv]

  31. R. Martin and C. Liu (2014). Discussion: Foundations of statistical inference, revisited. Statistical Science, volume 29, pages 247–251. [arXiv]

  32. R. Martin and C. Liu (2013). Inferential models: A framework for prior-free posterior probabilistic inference. Journal of the American Statistical Association, volume 108, pages 301–313. [arXiv] [R code]

    Correction. Journal of the American Statistical Association, volume 108, pages 1138–1139.

  33. R. Martin (2013). An approximate Bayesian marginal likelihood approach for estimating finite mixtures. Communications in Statistics–Simulation & Computation, volume 42, pages 1533–1548. [arXiv] [R code]

  34. R. Martin and O. Tilaks (2012). On ε-optimality of the pursuit learning algorithm. Journal of Applied Probability, volume 49, pages 795–805. [arXiv]

  35. R. Martin and S. T. Tokdar (2012). A nonparametric empirical Bayes framework for large-scale multiple testing. Biostatistics, volume 13, pages 427–439. (Earlier version won ASA–SBSS Student Paper Award.) [arXiv]

  36. R. Martin (2012). Convergence rate for predictive recursion estimation of finite mixtures. Statistics & Probability Letters, volume 82, pages 378–384. [arXiv]

  37. R. Martin and S. T. Tokdar (2011). Semiparametric inference in mixture models with predictive recursion marginal likelihood. Biometrika, volume 98, pages 567–582. [arXiv]

  38. Z. Zhang, H. Xu, R. Martin, and C. Liu (2011). Inferential models for linear regression. Pakistan Journal of Statistics and Operations Research, volume 7, pages 413–432; special issue on "Variable Selection in Regression".

  39. O. Tilaks, R. Martin, and S. Mukhopadhyay (2011). Decentralized indirect method for learning automata games. IEEE Transactions on Systems, Man, and Cybernetics–Part B, volume 41, pages 1213–1223.

  40. R. Martin, J. Zhang, and C. Liu (2010). Dempster–Shafer theory and statistical inference with weak beliefs. Statistical Science, volume 25, pages 72–87. [arXiv]

  41. R. Martin and S. T. Tokdar (2009). Asymptotic properties of predictive recursion: robustness and rate of convergence. Electronic Journal of Statistics, volume 3, pages 1455–1472.

  42. S. T. Tokdar, R. Martin, and J. K. Ghosh (2009). Consistency of a recursive estimate of mixing distributions. The Annals of Statistics, volume 37, pages 2502–2522. [arXiv]

  43. R. Martin and J. K. Ghosh (2008). Stochastic approximation and Newton's estimate of a mixing distribution. Statistical Science, volume 23, pages 365–382. [arXiv]

  44. 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. World Scientific, Hackensack, NJ, pages 154–161.


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