Ryan Martin Martin, R. (2024). Ryan Martin's 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-STATISTICAL METHODOLOGY, 86(1). https://doi.org/10.1093/jrsssb/qkad112 Wu, P.-S., & Martin, R. (2023). A Comparison of Learning Rate Selection Methods in Generalized Bayesian Inference. BAYESIAN ANALYSIS, 18(1), 105–132. https://doi.org/10.1214/21-BA1302 Dixit, V., & Martin, R. (2023). A PRticle filter algorithm for nonparametric estimation of multivariate mixing distributions. STATISTICS AND COMPUTING, 33(4). https://doi.org/10.1007/s11222-023-10242-2 Syring, N., & Martin, R. (2023). Gibbs posterior concentration rates under sub-exponential type losses. BERNOULLI, 29(2), 1080–1108. https://doi.org/10.3150/22-BEJ1491 Cella, L., & Martin, R. (2023). Possibility-theoretic statistical inference offers performance and probativeness assurances. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 163. https://doi.org/10.1016/j.ijar.2023.109060 Dixit, V., & Martin, R. (2023). Revisiting consistency of a recursive estimator of mixing distributions. ELECTRONIC JOURNAL OF STATISTICS, 17(1), 1007–1042. https://doi.org/10.1214/23-EJS2121 Hose, D., Hanss, M., & Martin, R. (2022). A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference. BELIEF FUNCTIONS: THEORY AND APPLICATIONS (BELIEF 2022), Vol. 13506, pp. 197–206. https://doi.org/10.1007/978-3-031-17801-6_19 Martin, R., & Syring, N. (2022). Direct Gibbs posterior inference on risk minimizers: Construction, concentration, and calibration. ADVANCEMENTS IN BAYESIAN METHODS AND IMPLEMENTATION, Vol. 47, pp. 1–41. https://doi.org/10.1016/bs.host.2022.06.004 Cella, L., & Martin, R. (2022). Direct and approximately valid probabilistic inference on a class of statistical functionals. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 151, 205–224. https://doi.org/10.1016/j.ijar.2022.09.011 Dixit, V., & Martin, R. (2022, February 10). Estimating a Mixing Distribution on the Sphere Using Predictive Recursion. SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS. https://doi.org/10.1007/s13571-021-00275-w Bhattacharya, I., & Martin, R. (2022). Gibbs posterior inference on multivariate quantiles. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 218, 106–121. https://doi.org/10.1016/j.jspi.2021.10.003 Cella, L., & Martin, R. (2022). Valid Inferential Models Offer Performance and Probativeness Assurances. BELIEF FUNCTIONS: THEORY AND APPLICATIONS (BELIEF 2022), Vol. 13506, pp. 219–228. https://doi.org/10.1007/978-3-031-17801-6_21 Mao, H., Martin, R., & Reich, B. J. J. (2022, December 17). Valid Model-Free Spatial Prediction. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 12. https://doi.org/10.1080/01621459.2022.2147531 Cella, L., & Martin, R. (2022). Valid inferential models for prediction in supervised learning problems. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 150, 1–18. https://doi.org/10.1016/j.ijar.2022.08.001 Cella, L., & Martin, R. (2022). Validity, consonant plausibility measures, and conformal prediction. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 141, 110–130. https://doi.org/10.1016/j.ijar.2021.07.013 Martin, R. (2021). A Survey of Nonparametric Mixing Density Estimation via the Predictive Recursion Algorithm. SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS, 83(1), 97–121. https://doi.org/10.1007/s13571-019-00206-w Cella, L., & Martin, R. (2021). Approximately Valid and Model-Free Possibilistic Inference. BELIEF FUNCTIONS: THEORY AND APPLICATIONS (BELIEF 2021), Vol. 12915, pp. 127–136. https://doi.org/10.1007/978-3-030-88601-1_13 Liu, C., Yang, Y., Bondell, H., & Martin, R. (2021). BAYESIAN INFERENCE IN HIGH-DIMENSIONAL LINEAR MODELS USING AN EMPIRICAL CORRELATION-ADAPTIVE PRIOR. STATISTICA SINICA, 31(4), 2051–2072. https://doi.org/10.5705/ss.202019.0133 Tokdar, S. T., & Martin, R. (2021). Bayesian Test of Normality Versus a Dirichlet Process Mixture Alternative. SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS, 83(1), 66–96. https://doi.org/10.1007/s13571-019-00210-0 Shi, W., Ghosal, S., & Martin, R. (2021). Bayesian estimation of sparse precision matrices in the presence of Gaussian measurement error. ELECTRONIC JOURNAL OF STATISTICS, 15(2), 4545–4579. https://doi.org/10.1214/21-EJS1904 Liu, C., & Martin, R. (2021, May). Comment: Settle the Unsettling: An Inferential Models Perspective. STATISTICAL SCIENCE, Vol. 36, pp. 196–200. https://doi.org/10.1214/21-STS765B Cahoon, J., & Martin, R. (2021). Generalized inferential models for censored data. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 137, 51–66. https://doi.org/10.1016/j.ijar.2021.06.015 Hong, L., & Martin, R. (2021, April 15). Imprecise credibility theory. ANNALS OF ACTUARIAL SCIENCE. https://doi.org/10.1017/S1748499521000117 Martin, R., Balch, M. S., & Ferson, S. (2021, June 30). Response to the comment Confidence in confidence distributions! https://doi.org/10.1098/rspa.2020.0579 Martin, R. (2021, April). Ryan Martin's contribution to the Discussion of 'Testing by betting: A strategy for statistical and scientific communication' by Glenn Shafer. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol. 184, pp. 456–457. https://doi.org/10.1111/rssa.12665 Martin, R. (2021). Towards a Theory of Valid Inferential Models with Partial Prior Information. BELIEF FUNCTIONS: THEORY AND APPLICATIONS (BELIEF 2021), Vol. 12915, pp. 137–146. https://doi.org/10.1007/978-3-030-88601-1_14 Hong, L., & Martin, R. (2021). Valid Model-Free Prediction of Future Insurance Claims. NORTH AMERICAN ACTUARIAL JOURNAL, 25(4), 473–483. https://doi.org/10.1080/10920277.2020.1802599 Martin, R., & Ning, B. (2020). Empirical Priors and Coverage of Posterior Credible Sets in a Sparse Normal Mean Model. SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY, 82(2), 477–498. https://doi.org/10.1007/s13171-019-00189-w Hong, L., & Martin, R. (2020). Model misspecification, Bayesian versus credibility estimation, and Gibbs posteriors. SCANDINAVIAN ACTUARIAL JOURNAL, 2020(7), 634–649. https://doi.org/10.1080/03461238.2019.1711154 Wang, Z., & Martin, R. (2020). Model-free posterior inference on the area under the receiver operating characteristic curve. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 209, 174–186. https://doi.org/10.1016/j.jspi.2020.03.008 Syring, N., & Martin, R. (2020). ROBUST AND RATE-OPTIMAL GIBBS POSTERIOR INFERENCE ON THE BOUNDARY OF A NOISY IMAGE. ANNALS OF STATISTICS, 48(3), 1498–1513. https://doi.org/10.1214/19-AOS1856 Martin, R., & Walker, S. G. (2019). Data-driven priors and their posterior concentration rates. ELECTRONIC JOURNAL OF STATISTICS, 13(2), 3049–3081. https://doi.org/10.1214/19-EJS1600 Martin, R. (2019). Discussion of 'Nonparametric generalized fiducial inference for survival functions under censoring'. BIOMETRIKA, 106(3), 519–522. https://doi.org/10.1093/biomet/asz022 Martin, R. (2019). Empirical Priors and Posterior Concentration Rates for a Monotone Density. Sankhya A, 81(2), 493–509. https://doi.org/10.1007/s13171-018-0147-5 Martin, R. (2019, October). False confidence, non-additive beliefs, and valid statistical inference. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, Vol. 113, pp. 39–73. https://doi.org/10.1016/j.ijar.2019.06.005 Syring, N., Hong, L., & Martin, R. (2019). Gibbs posterior inference on value-at-risk. SCANDINAVIAN ACTUARIAL JOURNAL, (7), 548–557. https://doi.org/10.1080/03461238.2019.1573754 Syring, N., & Martin, R. (2019). Miscellanea Calibrating general posterior credible regions. BIOMETRIKA, 106(2), 479–486. https://doi.org/10.1093/biomet/asy054 Lin, Y., Martin, R., & Yang, M. (2019). ON OPTIMAL DESIGNS FOR NONREGULAR MODELS. ANNALS OF STATISTICS, 47(6), 3335–3359. https://doi.org/10.1214/18-AOS1780 Chae, M., Martin, R., & Walker, S. G. (2019). On an algorithm for solving Fredholm integrals of the first kind. STATISTICS AND COMPUTING, 29(4), 645–654. https://doi.org/10.1007/s11222-018-9829-z Hong, L., & Martin, R. (2019). Real-time Bayesian non-parametric prediction of solvency risk. ANNALS OF ACTUARIAL SCIENCE, 13(1), 67–79. https://doi.org/10.1017/S1748499518000039 Balch, M. S., Martin, R., & Ferson, S. (2019). Satellite conjunction analysis and the false confidence theorem. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 475(2227). https://doi.org/10.1098/rspa.2018.0565 Tennant, J. P., Crane, H., Crick, T., Davila, J., Enkhbayar, A., Havemann, J., … Vanholsbeeck, M. (2019). [Review of Ten Hot Topics around Scholarly Publishing]. PUBLICATIONS, 7(2). https://doi.org/10.3390/publications7020034 Martin, R., Ouyang, C., & Domagni, F. (2018). 'Purposely misspecified' posterior inference on the volatility of a jump diffusion process. STATISTICS & PROBABILITY LETTERS, 134, 106–113. https://doi.org/10.1016/j.spl.2017.10.013 Chae, M., Martin, R., & Walker, S. G. (2018). Convergence of an iterative algorithm to the nonparametric MLE of a mixing distribution. STATISTICS & PROBABILITY LETTERS, 140, 142–146. https://doi.org/10.1016/j.spl.2018.05.012 Hong, L., & Martin, R. (2018). Dirichlet process mixture models for insurance loss data. SCANDINAVIAN ACTUARIAL JOURNAL, (6), 545–554. https://doi.org/10.1080/03461238.2017.1402086 Hahn, P. R., Martin, R., & Walker, S. G. (2018). On Recursive Bayesian Predictive Distributions. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 113(523), 1085–1093. https://doi.org/10.1080/01621459.2017.1304219 Martin, R. (2018). On an inferential model construction using generalized associations. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 195, 105–115. https://doi.org/10.1016/j.jspi.2016.11.006 Hong, L., Kuffner, T. A., & Martin, R. (2018). On overfitting and post-selection uncertainty assessments. BIOMETRIKA, 105(1), 221–224. https://doi.org/10.1093/biomet/asx083 Martin, R. (2017). A Statistical Inference Course Based on p-Values. AMERICAN STATISTICIAN, 71(2), 128–136. https://doi.org/10.1080/00031305.2016.1208629 Hong, L., & Martin, R. (2017). [Review of A review of Bayesian asymptotics in general insurance applications]. EUROPEAN ACTUARIAL JOURNAL, 7(1), 231–255. https://doi.org/10.1007/s13385-017-0151-5 Liu, C., Martin, R., & Syring, N. (2017). Efficient simulation from a gamma distribution with small shape parameter. COMPUTATIONAL STATISTICS, 32(4), 1767–1775. https://doi.org/10.1007/s00180-016-0692-0 Syring, N., & Martin, R. (2017). Gibbs posterior inference on the minimum clinically important difference. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 187, 67–77. https://doi.org/10.1016/j.jspi.2017.03.001 Martin, R. (2017). Uncertainty quantification for the horseshoe (with discussion) comment. Bayesian Analysis, 12(4), 1254–1258. Martin, R., Stufken, J., & Yang, M. (2016). A Conversation with Samad Hedayat. STATISTICAL SCIENCE, 31(4), 637–647. https://doi.org/10.1214/16-sts579 Martin, R., & Han, Z. (2016). A semiparametric scale-mixture regression model and predictive recursion maximum likelihood. Computational Statistics & Data Analysis, 94, 75–85. https://doi.org/10.1016/J.CSDA.2015.08.005 Martin, R., & Lin, Y. (2016). Exact prior-free probabilistic inference in a class of non-regular models. Stat, 5(1), 312–321. https://doi.org/10.1002/STA4.130 Martin, R., & Lingham, R. T. (2016). Prior-Free Probabilistic Prediction of Future Observations. Technometrics, 58(2), 225–235. https://doi.org/10.1080/00401706.2015.1017116 Martin, R. (2015). Asymptotically Optimal Nonparametric Empirical Bayes Via Predictive Recursion. Communications in Statistics - Theory and Methods, 44(2), 286–299. https://doi.org/10.1080/03610926.2012.743566 Liu, C., & Martin, R. (2015). Frameworks for prior-free posterior probabilistic inference. Wiley Interdisciplinary Reviews: Computational Statistics, 7(1), 77–85. https://doi.org/10.1002/WICS.1329 Martin, R., & Liu, C. (2015). Marginal Inferential Models: Prior-Free Probabilistic Inference on Interest Parameters. Journal of the American Statistical Association, 110(512), 1621–1631. https://doi.org/10.1080/01621459.2014.985827 Martin, R. (2015). Plausibility Functions and Exact Frequentist Inference. Journal of the American Statistical Association, 110(512), 1552–1561. https://doi.org/10.1080/01621459.2014.983232 Martin, R. (2014). Random Sets and Exact Confidence Regions. Sankhya A, 76(2), 288–304. https://doi.org/10.1007/S13171-013-0046-8 Martin, R. (2013). An Approximate Bayesian Marginal Likelihood Approach for Estimating Finite Mixtures. Communications in Statistics - Simulation and Computation, 42(7), 1533–1548. https://doi.org/10.1080/03610918.2012.667476 Martin, R., & Liu, C. (2013). Correction. Journal of the American Statistical Association, 108(503), 1138–1139. https://doi.org/10.1080/01621459.2013.796885 Martin, R., & Liu, C. (2013). Inferential Models: A Framework for Prior-Free Posterior Probabilistic Inference. Journal of the American Statistical Association, 108(501), 301–313. https://doi.org/10.1080/01621459.2012.747960 Martin, R. (2012). Convergence rate for predictive recursion estimation of finite mixtures. Statistics & Probability Letters, 82(2), 378–384. https://doi.org/10.1016/j.spl.2011.10.023 Martin, R., & Tilak, O. (2012). On ε-Optimality of the Pursuit Learning Algorithm. Journal of Applied Probability, 49(03), 795–805. https://doi.org/10.1017/S0021900200009542