Steve MacEachern

PAPERS SINCE 2010

  • MacEachern, S.N. (2016). Nonparametric Bayesian methods: a gentle introduction and overview. Communications for Statistical Applications and Methods 6, 445-466.

  • Som, A., Hans, C.M., and MacEachern, S.N. (2016). A conditional Lindley paradox in Bayesian linear models. Biometrika 103, 993-999.

  • Lee, J., MacEachern, S.N., Lu, Y., and Mills, G.B. (2014). Local mass preserving prior distributions for nonparametric Bayesian models. Bayesian Analysis 9, 307-330.

  • Jung, Y., Lee, Y., and MacEachern, S.N. (2015). Efficient quantile regression for heteroscedastic models. Journal of Statistical Computation and Simulation 85, 2548-2568.

  • Kim, H., and MacEachern, S.N. (2015). The generalized multiset sampler. Journal of Computational and Graphical Statistics 24, 1134-1154.

  • Kadane, J., and MacEachern, S.N. (2014). Toward rational social decisions: a review and some results. Bayesian Analysis 9, 685-698.

  • Lee, J., and MacEachern, S.N. (2014). Inference functions in high dimensional Bayesian inference. Statistics and Its Interface 7, 477-486.

  • Williamson, S.A., MacEachern, S.N., and Xing, E. (2013). Restricting exchangeable nonparametric distributions. NIPS 2013, 2598-2606.

  • Xu, Z., MacEachern, S.N., and Xu, X. (2015). Modeling time series with a nonparametric Bayesian model. IEEE-Transactions on Pattern Analysis and Machine Intelligence 37, 372-382.

  • Yu, Q., MacEachern, S.N., and Peruggia, M. (2013). Clustered Bayesian model averaging. Bayesian Analysis 8, 883-908.

  • Lee, J., and MacEachern, S.N., Lu, Y., and Mills, G.B. (2013). Local mass preserving prior distributions for nonparametric Bayesian models. Bayesian Analysis 8, 1-24.

  • Ozturk, O., and MacEachern, S.N. (2013). Inference based on general linear models for order restricted randomization. Communications in Statistics: Theory and Methods 42, 3243-3266.

  • Hans, C., Craigmile, P.F., Lee, J., MacEachern, S.N., and Xu, X. (2012). Covariance decompositions for accurate computation in Bayesian scale-usage models. Journal of Computational and Graphical Statistics 21, 538-557.

  • Paul, R., Berliner, L.M., and MacEachern, S.N. (2012). Assessing convergence and mixing of MCMC implementations via stratification. Journal of Computational and Graphical Statistics 21, 693-712.

  • Lee, Y., MacEachern, S.N., and Jung, Y. (2012). Regularization of case-specific parameters for robustness and efficiency. Statistical Science 27, 350-372.

  • Yu, Q., MacEachern, S.N., and Peruggia, M. (2011). Bayesian Synthesis: Combining subjective analyses, with an application to ozone data, The Annals of Applied Statistics 5, 1678-1698.

  • MacEachern, S.N., and Guha, S. (2011). Parametric and semiparametric hypotheses in the linear model, Canadian Journal of Statistics 39, 165-180.

  • Westman, J.A., Ferketich, A.K., Kauffman, R.M., MacEachern, S.N., Wilkins, J.R. III, Wilcox, P.P., Pilarski, R.T., Nagy, R., Lemeshow, S., de la Chapelle, A., and Bloomfield, C.D. (2010). Low cancer incidence rates in Ohio Amish, Cancer Causes Control 21, 69-75.

  • Bush, C.A., Lee, J., and MacEachern, S.N. (2010). Minimally informative prior distributions for non-parametric Bayesian analysis, Journal of the Royal Statistical Society: Series B 72, 253-268.

  • Lee, J., and MacEachern, S.N. (2010). Consistency of Bayes estimators without the assumption that the model is correct, Journal of Statistical Planning and Inference 141, 748-757.

BOOK CHAPTERS, DISCUSSIONS, NON-REFEREED PAPERS, ETC. SINCE 2010

  • Houpt, J.W., MacEachern, S.N., Peruggia, M., Townsend, J.T., Van Zandt, T. (2016). Semiparametric Bayesian approaches to systems factorial technology. Journal of Mathematical Psychology 75, 68-85.

  • MacEachern, S.N. (2014). Comment on Finegold and Drton's ``Robust Bayesian graphical modeling using Dirichlet t-distributions''. Bayesian Analysis 9, 574-576.

  • Duncan, K.A., and S.N. MacEachern (2012). Nonparametric Bayesian modelling of item response curves with a three parameter logistic prior mean. In ``Current Topics in the Theory and Application of Latent Variable Models'' M.C. Edwards and R.C. MacCallum (eds.), Taylor & Francis.

  • Lewis, J., Lee, Y., and MacEachern, S.N. (2012). ``Robust inference via the blended paradigm'', in the American Statistical Association 2012 Proceedings.

MANUSCRIPTS UNDER SUBMISSION

    I currently have quite a few manuscripts under submission or revision, including:

  • Xu, X., P. Lu, MacEachern, S.N., and Xu, R. Calibrated Bayes factors for model comparison.

  • Lewis, J.R., Lee, Y., and MacEachern, S.N. Bayesian restricted likelihood methods.

  • Som, A., Hans, C.M., and MacEachern, S.N. Block hyper-g priors in Bayesian regression.

Refer to CV for the full list of publications.