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Research areas
I work in several areas of statistics theory and methodology including Bayesian and pseudo-Bayesian statistics, foundations of statistical inference, and post-selection inference. Links to my published papers and drafts may be found below.
- N. Syring and R. Martin. Robust and Rate-Optimal Gibbs Posterior Inference on the Boundary of a Noisy Image. Annals of Statistics. (2019). Accepted. https://arxiv.org/abs/1606.08400v3. https://www.imstat.org/journals-and-publications/annals-of-statistics/annals-of-statistics-future-papers/
- R. Martin and N. Syring. Validity-preservation properties of rules for combining inferential models. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, in Proceedings of Machine Learning Research. (2019), 103:286-294. http://proceedings. mlr.press/v103/martin19a/martin19a.pdf
- N. Syring and R. Martin. Calibrating General Posterior Credible Regions. Biometrika. (2018). https://doi.org/10.1093/biomet/asy054
- N. Syring L. Hong, and R. Martin. Gibbs Posterior Inference on Value-at-Risk. Scandinavian Actuarial Journal. (2019). https://doi.org/10.1080/03461238.2019.1573754
- N. Syring and R. Martin. Gibbs Posterior Inference on the Minimum Clinically Important Difference. Journal of Statistical Planning and Inference. 187 (2017): 67-77. http://dx.doi.org/10.1016/j.jspi.2017.03.001
- C. Liu, R. Martin, and N. Syring. Efficient Simulation from a Gamma Distribution with Small Shape Parameter. Computational Statistics 32, 4 (2017): 1767-1775. https://doi.org/10.1007/s00180-016-0692-0
- N. Syring and M. Li. BayesBD: An R Package for Bayesian Inference on Image Boundaries. R Journal. 9, 2 (2017): 149-162. https://journal.r-project.org/archive/2017/RJ-2017-052/index.html