Under construction – a few recent papers, with others slowly being added. For most of the papers, no clickable link yet. Most are available on JSTOR, journal web sites, or elsewhere. A few are difficult to get ahold of
A few papers that have found some popularity but seem to be hard to find. I’m working to find better copies of some. If the pdf is difficult to read, you might try a different pdf reader or printing a hard copy. Alternatively, email me and I will be happy to send a pdf that looks good when I pop it open
Dependent nonparametric processes. MacEachern, S.N. (1999). This one appeared in the ASA’s (unrefereed) JSM Proceedings. https://u.osu.edu/maceachern.1/files/2025/05/1999-MacEachern-JSM-Proceedings.pdf
Dependent Dirichlet processes. MacEachern, S.N. (2000). This one is a Technical Report at The Ohio State University. https://u.osu.edu/maceachern.1/files/2025/05/2000-MacEachern-DDP-Tech-Report.pdf
Spatial nonparametric Bayesian models. MacEachern, S.N., Kottas, T., and Gelfand, A.E. (2001). This one appeared in the ASA’s (unrefereed) JSM Proceedings. https://u.osu.edu/maceachern.1/files/2025/05/2001-MacEachernKottasGelfand-JSM-Proceedings.pdf
Robust inference via the blended paradigm. Lewis, J., Lee, Y., and MacEachern, S.N. (2012). This one appeared in the ASA’s (unrefereed) JSM Proceedings. https://u.osu.edu/maceachern.1/files/2025/05/2012-LewisLeeMacEachern-JSM-Proceedings.pdf
Refereed journal articles and book chapters
Bayesian model calibration and sensitivity analysis for oscillating biological experiments. Hwang, Y., Kim, H.J., Chang, W., Hong, C., and MacEachern, S.N. (2025). Technometrics, 1-11.
melt: Multiple Empirical Likelihood Tests in R. Kim, E., MacEachern, S.N., and Peruggia, M. (2024). Journal of Statistical Software 108, 1-33.
A process of dependent quantile pyramids. An, H., and MacEachern, S.N. (2024). Journal of Nonparametric Statistics, 1-25.
Familial inference: tests for hypotheses on a family of centres. Thompson, R., Forbes, C.S., MacEachern, S.N., and Peruggia, M. (2023). Biometrika 111, 1029-1045.
Empirical likelihood for the analysis of experimental designs. Kim, E., MacEachern, S.N., and Peruggia, M. (2023). Journal of Nonparametric Statistics 35, 709-732.
Bridging the design and modeling of causal inference: A Bayesian nonparametric perspective. Xu, X., MacEachern, S.N., and Lu, B. (2023). Observational Studies 9, 119-124.
Economic variable selection. Miyawaki, K. and MacEachern, S.N. (2023). Canadian Journal of Statistics 51, 19-37.
Rediscovering a little known fact about the t-test and the F-test: algebraic, geometric, distributional and graphical considerations. Sinnott, J.A., MacEachern, S.N., and Peruggia, M. (2022). Statistica 82, 79-96.
Predictive modelling and judgement post-stratification. MacEachern, S.N. and Kim, J. (2022). In “Recent Advances in Sampling Methods and Educational Statistics, In Honor of S. Lynne Stokes“, H.K.T. Ng, D.F. Heitjan (eds.), Springer: 3-20
A regression approach to the two-dataset problem. MacEachern, S.N., and Miyawaki, K. (2022). Statistics 56, 1225-1241.
The dependent Dirichlet process and related models. Quintana, F., Mueller, P., Jara, A., and MacEachern, S.N. (2022). Statistical Science 37, 24-41.
Forcing a model to be correct for classification. Kim, J. and MacEachern, S.N. (2021). NeurIPS 2021 Workshop: Your model is wrong: robustness and misspecification in probabilistic modeling. [Paper accompanied a poster presentation at the workshop. Nonstandard refereeing process. Available at https://drive.google.com/file/d/11ICgdS0CMEwYfk9qo0B6Otn-m9d9iUeZ/view ]
Bayesian restricted likelihood methods: Conditioning on insufficient statistics in Bayesian regression (with discussion and rejoinder). Lewis, J.R., MacEachern, S.N., and Lee, Y. (2021). Bayesian Analysis 16, 1393-1462.
Aggregated pairwise classification of elastic planar shapes. Cho, M.H., Kurtek, S. and MacEachern, S.N. (2021). Annals of Applied Statistics 15, 619-637.
A class of generalized linear mixed models adjusted for marginal interpretability. Gory, J.J., Craigmile, P.F., and MacEachern, S.N. (2021). Statistics in Medicine 40, 427-440.
Modified check loss for efficient estimation via model selection in quantile regression. Jung, Y., MacEachern, S.N., and Kim, H.J. (2020). Journal of Applied Statistics 48, 866-886.
A new proof of the stick-breaking representation of Dirichlet processes. Lee, J., and MacEachern, S.N. (2020). Journal of the Korean Statistical Society 49, 389-394.
Discussions
Discussion of “Evaluating sensitivity to the stick-breaking prior in Bayesian nonparametrics” by Giordano, Liu, Jordan and Broderick. MacEachern, S.N., and Lee, J. (2023). Bayesian Analysis 18, 321-324.