Publications

Research Monograph

  • Peruggia, M. (1993), Discrete Iterated Function Systems, Wellesley, MA: AK Peters. A Japanese translation of the monograph was published in 1995 by Toppan Company, Ltd.

Recent Manuscripts and Manuscripts Under Review

Selected Peer Reviewed Articles

  • Kunkel, D. and Peruggia,  M., (2025), “Statistical Inference With Anchored Bayesian Mixture of Regressions Models: An Illustrative Study of Allometric Data,” Statistica Sinica, 35, doi:10.5705/ss.202021.0387, Preprint.
  • Kim, E., MacEachern, S. N., and Peruggia, M. (2024), “melt: Multiple Empirical Likelihood Tests in R,” Journal of Statistical Software, 108(5), 1–33. https://doi.org/10.18637/jss.v108.i05.
  • Thompson, R., Forbes, C.S., MacEachern, S.N., and Peruggia, M., (2023), “Familial inference: Tests for hypotheses on a family of centres,” Biometrika, asad074, https://doi.org/10.1093/biomet/asad074.
  • Luo, H., MacEachern, S., and Peruggia, M., (2023), “Asymptotics of Lower Dimensional Zero-Density Regions,” Statistics, https://doi.org/10.1080/02331888.2023.2262665.
  • Kim, E., MacEachern, S., and Peruggia, M. (2023), “Empirical Likelihood for the Analysis of Experimental Designs,” Journal of Nonparametric Statistics, 35, 4, 709–732. https://doi.org/10.1080/10485252.2023.2206919.
  • Hans, C. M., Peruggia, M. and Wang, J. (2023), “Empirical Bayes Model Averaging with Influential Observations: Tuning Zellner’s g Prior for Predictive Robustness,” Econometrics and Statistics, 27, 102-119, Available online 18 December 2021, https://doi.org/10.1016/j.ecosta.2021.12.003.
  • Chen, Y., Peruggia, M., and Van Zandt, T. (2022), “Mutual interference in working memory updating: a hierarchical Bayesian model,” Journal of Mathematical Psychology, 111, 102706. https://doi.org/10.1016/j.jmp.2022.102706.
  • Sinnott, J. A., MacEachern, S. N., & Peruggia, M. (2022), “Rediscovering a Little Known Fact about the t-test and the F-test: Algebraic, Geometric, Distributional and Graphical Considerations,” Statistica, 82, 79–96. https://doi.org/10.6092/issn.1973-2201/13560.
  • Chen, Y., Breitborde, N.J.K., Peruggia, M., and Van Zandt, T., (2022), “Understanding motivation with the progressive ratio task: A hierarchical Bayesian model,”  Computational Brain & Behavior, https://doi.org/10.1007/s42113-021-00114-1.
  • Kunkel, D., Yan, Z., Peruggia, M., Craigmile, P.F., and Van Zandt, T., (2021), “Hierarchical Hidden Markov Models for Response Time Data,” Computational Brain & Behavior, https://doi.org/10.1007/s42113-020-00076-w. Supplemental material.
  • Kunkel, D. and Peruggia,  M., (2020), “Anchored Bayesian Gaussian Mixture Models,” Electronic Journal of Statistics, 14, 3869-3913. doi:10.1214/20-EJS1756. https://projecteuclid.org/euclid.ejs/1603353627.
  • Kunkel, D., Potter, K., Craigmile, P., Peruggia, M., and Van Zandt, T., (2019), “A Bayesian Race Model for Response Times under Cyclic Stimulus Discriminability,” Annals of Applied Statistics, 13, 271-296, http://dx.doi.org/10.1214/18-AOAS1192.
  • Thomas, Z.M., MacEachern, S.N., and Peruggia, M. (2018), “Reconciling Curvature and Importance Sampling Based Procedures for Summarizing Case Influence in Bayesian Modeling,” Journal of the American Statistical Association, 113, 1669-1683,  http://dx.doi.org/10.1080/01621459.2017.1360777.
  • Kim, S., Potter, K., Craigmile, P.F., Peruggia, M., and Van Zandt, T. (2017), “A Bayesian Race Model for Recognition Memory,” Journal of the American Statistical Association, 112, 77—91, https://doi.org/10.1080/01621459.2016.1194844 (Supplementary material posted online at https://github.com/petercraigmile/BayesianRaceModel)
  • Houpt, J.W., MacEachern, S.N., Peruggia, M., Townsend, J.T., and Van Zandt, T. (2016), “Semiparametric Bayesian Approaches to Systems Factorial Technology,” Journal of Mathematical Psychology, 75, 68—85, https://doi.org/doi:10.1016/j.jmp.2016.02.008.
  • Sonksen, M.D. and Peruggia, M. (2014), “Inferences on lung cancer mortality rates based on reference priors under partial ordering,” Journal of the Royal Statistical Society: Series C (Applied Statistics), 63 (5), 783—800,  https://doi.org/doi:10.1111/rssc.12059.
  • Yu, Q., MacEachern, S. N., and Peruggia, M. (2013), “Clustered Bayesian model averaging,” Bayesian Analysis, 8 (4), 741—908,  https://doi.org/doi:10.1214/13-BA859.
  • Peruggia, M., Hsu, J. C., and Huang, Y. (2013), “Cartesian Displays of Many Interval Estimates,” Electronic Journal of Statistics, 7, 91—104, https://doi.org/doi:10.1214/12-EJS761.
  • Sonksen, M. and Peruggia, M. (2012), “Reference Priors for Constrained Rate Models of Count Data,” Journal of Statistical Planning and Inference, 142, 11, 3023—3036, https://doi.org/doi:10.1016/j.jspi.2012.04.015.
  • Yu, Q., MacEachern, S.N., and Peruggia, M. (2011), “Bayesian Synthesis: Combining subjective analyses, with an application to ozone data,” Annals of Applied Statistics, 5, 2B, 1678—1698, https://doi.org/doi:10.1214/10-AOAS444.
  • Craigmile, P.F., Peruggia, M, and Van Zandt, T. (2010), “Hierarchical Bayes Models for Response Time Data,” Psychometrika, 75, 613—632, https://doi.org/doi:10.1007/s11336-010-9172-6.
  • Epifani, I., MacEachern, S., and Peruggia, M. (2008), “Case-Deletion Importance Sampling Estimators: Central Limit Theorems and Related Results,” Electronic Journal of Statistics, 2, 774—806, https://doi.org/doi:10.1214/08-EJS259.
  • Peruggia, M. (2007), “Bayesian Model Diagnostics Based on Artificial Autoregressive Errors,” Bayesian Analysis, 2, 817—842, https://doi.org/doi:10.1214/07-BA233.
  • Huang, Y., Hsu, J.C., Peruggia, M. and Scott, A.A. (2006), “Statistical Selection of Maintenance Genes for Normalization of Gene Expressions,” Statistical Applications in Genetics and Molecular Biology, Vol. 5: No. 1, Article 4, https://doi.org/10.2202/1544-6115.1122.
  • Guha, S., MacEachern, S.N., and Peruggia, M. (2004), “Benchmark Estimation for Markov Chain Monte Carlo Samples,” Journal of Computational and Graphical Statistics, 13, 683—701, https://doi.org/10.1198/106186004X2598.
  • Peruggia, M., Santner, T.J., and Ho, Y.-Y. (2004), “Detecting Stage-Wise Outliers in Hierarchical Bayesian Linear Models of Repeated Measures Data,” Annals of the Institute of Statistical Mathematics, 56, 415—433, https://doi.org/10.1007/BF02530534.
  • Peruggia, M., Van Zandt, T., and Chen, M. (2002), “Was it a car or a cat I saw? An Analysis of Response Times for Word Recognition,” Case Studies in Bayesian Statistics, Vol. 6, New York: Springer-Verlag, 319—334.
  • MacEachern, S.N., and Peruggia, M. (2002), “Bayesian Tools for EDA and Model Building: A Brainy Study,” Case Studies in Bayesian Statistics, Vol. 5, 345—362, New York: Springer-Verlag.
  • MacEachern, S.N., and Peruggia, M. (2000), “Importance Link Function Estimation for Markov Chain Monte Carlo Methods,” Journal of Computational and Graphical Statistics, 9, 99—121.
  • MacEachern, S.N., and Peruggia, M. (2000), “Subsampling the Random Scan Gibbs Sampler: Variance Reduction,” Statistics and Probability Letters, 47, 91—98.
  • Goel, P., Peruggia, M., and An, B. (1997), “Computer Aided Teaching of Probabilistic Modeling for Biological Phenomena,” The American Statistician, 51, 164—169.
  • Peruggia M. (1997), “On the Variability of Case-Deletion Importance Sampling Weights in the Bayesian Linear Model,” Journal of the American Statistical Association, 92, 199—207.
  • Peruggia M., and Santner, T. (1996), “Bayesian Analysis of Time Evolution of Earthquakes,” Journal of the American Statistical Association, 91, 1209—1218.
  • Hsu, J., and Peruggia, M. (1994), “Graphical Representations of Tukey’s Multiple Comparison Method,” Journal of Computational and Graphical Statistics, 3, 143—161.
  • Peruggia, M., Santner, T., Ho, Y.Y., and McMillan, N. (1994), “A Hierarchical Bayesian Analysis of Circular Data with Autoregressive Errors: Modeling the Mechanical Properties of Cortical Bone,” Statistical Decision Theory and Related Topics V, 201—220, New York: Springer-Verlag.

Invited Book Chapters (Editor reviewed)

  • Craigmile, P.F., Peruggia, M. and Van Zandt, T. (2013), “A Bayesian Hierarchical Model for Response Time Data Providing Evidence for Criteria Changes Over Time,” in M.C. Edwards and R.C. MacCallum (Eds.) Current topics in the theory and application of latent variable models, 42—61, New York: Taylor and Francis.
  • Craigmile, P.F., Peruggia, M., and Van Zandt, T. (2010), “Detrending Response Time Series,” in S.M. Chow, E. Ferrer, and F. Hsieh (Eds.), Statistical methods for modeling human dynamics: An interdisciplinary dialogue, Notre Dame Series on Quantitative Methodology (Vol. 4), 213—240, New York, NY: Taylor and Francis.
  • Peruggia, M., Sun, J., Mullins, M., and Suratt, P. (2004), “Wavelet Modeling and Processing of Nasal Airflow Traces,” Methods in Enzymology: Numerical Computer Methods, Part E, 384, 106—130.

Invited Discussions

  • Hans, C.M. and Peruggia, M. (2015). “Comment on Article by Dawid and Musio.” Invited discussion of “Bayesian Model Selection Based on Proper Scoring Rules” by A. Philip Dawid and M. Musio. Bayesian Analysis, 10, (2), 505{509. doi: 10.1214/15-BA942B.
  • MacEachern, S.N., Peruggia, M., and Guha, S. (2003). Discussion of “A Theory of Statistical Models for Monte Carlo Integration,” by Kong, A., McCullagh, P., Nicolae, D., Tan, Z., and Meng, X.-L., Journal of the Royal Statistical Society, Series B, 65, 612.