Andrés Felipe Barrientos

Associate Professor
Department of Statistics
Florida State University

Refereed Journals

  1. Jauch, M., Barrientos, A. F., Peña, V., and Matteson, D. S. (2026). Mixture representations and Bayesian nonparametric inference for likelihood ratio ordered distributions. Bayesian Analysis, 21(3):1277–1300. [Journal] [arXiv]
  2. Guzman, J., Murphy, J. W., Beasor, E., Dalcanton, J. J., Smith, N., Aghakhanloo, M., Williams, B. F., and Barrientos, A. F. (2026). Quantifying systematic age discrepancies in very young star clusters. The Astrophysical Journal, 1008:99. [Journal] [arXiv]
  3. Guo, Q., Barrientos, A. F., and Peña, V. (2025). Differentially private methods for compositional data. Journal of Computational and Graphical Statistics, 34(3):848–858. [Journal] [arXiv]
  4. Peña, V. and Barrientos, A. F. (2025). Differentially private hypothesis testing with the subsampled and aggregated randomized response mechanism. Statistica Sinica, 35(Online Special Issue I):671–691. [Journal] [arXiv]
  5. Iguchi, T., Barrientos, A. F., Chicken, E., and Sinha, D. (2025). Profile monitoring via eigenvector perturbation. Technometrics, 67(2):283–292. [Journal] [arXiv]
  6. Williams, A. R., Snoke, J., Bowen, C. M., and Barrientos, A. F. (2025). Disclosing economists' privacy perspectives: A survey of American Economic Association members' views on differential privacy and the usability of noise-infused data. Harvard Data Science Review, Special Issue 6. [Journal]
  7. Guzman, J. J., Murphy, J. W., Barrientos, A. F., Williams, B. F., and Dalcanton, J. J. (2025). Stellar ages: A code to infer properties of stellar populations. The Astrophysical Journal, 986(1):83. [Journal] [arXiv]
  8. Murphy, J. W., Barrientos, A. F., Andrae, R., Guzman, J. J., Williams, B. F., Dalcanton, J. J., and Koplitz, B. (2025). The mass of the Vela pulsar progenitor and the age of the Vela-Puppis complex. The Astrophysical Journal, 988(2):241. [Journal] [arXiv]
  9. Barrientos, A. F., Williams, A. R., Snoke, J., and Bowen, C. M. (2024). A feasibility study of differentially private summary statistics and regression analyses with evaluations on administrative and survey data. Journal of the American Statistical Association, 119(545):52–65. [Journal] [arXiv]
  10. Peña, V. and Barrientos, A. F. (2024). Differentially private methods for managing model uncertainty in linear regression. Journal of Machine Learning Research, 25(74):1–44. [Journal] [arXiv]
  11. Barrientos, A. F., Page, G. L., and Lin, L. (2024). Non-parametric Bayesian approach to multiple treatment comparisons in network meta-analysis with application to comparisons of anti-depressants. Journal of the Royal Statistical Society Series C: Applied Statistics, 73(5):1333–1354. [Journal] [arXiv]
  12. Snoke, J., Bowen, C. M., Williams, A. R., and Barrientos, A. F. (2024). Incompatibilities between current practices in statistical data analysis and differential privacy. Journal of Privacy and Confidentiality, 14(3). [Journal] [arXiv]
  13. Barrientos, A. F., Sen, D., Page, G. L., and Dunson, D. B. (2023). Bayesian inferences on uncertain ranks and orderings: application to ranking players and lineups. Bayesian Analysis, 18(3):777–806. [Journal]
  14. Wehrhahn, C., Barrientos, A. F., and Jara, A. (2022). Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials. Electronic Journal of Statistics, 16(1):2346–2405. [Journal]
  15. Nixon, M. P., Barrientos, A. F., Reiter, J. P., and Slavković, A. (2022). A latent class modeling approach for generating synthetic data and making posterior inferences from differentially private counts. Journal of Privacy and Confidentiality, 12(1). [Journal]
  16. Wehrhahn, C., Jara, A., and Barrientos, A. F. (2021). On the small sample behavior of Dirichlet process mixture models for data supported on compact intervals. Communications in Statistics - Simulation and Computation, 50(3):786–810. [Journal]
  17. Barrientos, A. F. and Canale, A. (2021). A Bayesian goodness-of-fit test for regression. Computational Statistics & Data Analysis, 155:107104. [Journal]
  18. Iguchi, T., Barrientos, A. F., Chicken, E., and Sinha, D. (2021). Nonlinear profile monitoring with single index models. Quality and Reliability Engineering International, 37(7):3004–3017. [Journal]
  19. Barrientos, A. F. and Peña, V. (2020). Bayesian bootstraps for massive data. Bayesian Analysis, 15(2):363–388. [Journal]
  20. Barrientos, A. F., Reiter, J. P., Machanavajjhala, A., and Chen, Y. (2019). Differentially private significance tests for regression coefficients. Journal of Computational and Graphical Statistics, 28(2):440–453. [Journal]
  21. Gutiérrez, L., Barrientos, A. F., González, J., and Taylor-Rodríguez, D. (2019). A Bayesian nonparametric multiple testing procedure for comparing several treatments against a control. Bayesian Analysis, 14(2):649–675. [Journal]
  22. Akande, O., Barrientos, A. F., and Reiter, J. P. (2019). Simultaneous edit and imputation for household data with structural zeros. Journal of Survey Statistics and Methodology, 7(4):498–519. [Journal]
  23. Akande, O., Reiter, J. P., and Barrientos, A. F. (2019). Multiple imputation of missing values in household data with structural zeros. Survey Methodology, 45(2):271–294. [Journal]
  24. Barrientos, A. F., Bolton, A., Balmat, T., Reiter, J. P., de Figueiredo, J. M., Machanavajjhala, A., Chen, Y., Kneifel, C., and DeLong, M. (2018). Providing access to confidential research data through synthesis and verification: An application to data on employees of the U.S. federal government. The Annals of Applied Statistics, 12(2):1124–1156. [Journal]
  25. Chen, Y., Barrientos, A. F., Machanavajjhala, A., and Reiter, J. P. (2018). Is my model any good: differentially private regression diagnostics. Knowledge and Information Systems, 54(1):33–64. [Journal]
  26. Barrientos, A. F., Jara, A., and Quintana, F. A. (2017). Fully nonparametric regression for bounded data using dependent Bernstein polynomials. Journal of the American Statistical Association, 112(518):806–825. [Journal]
  27. Barrientos, A. F., Jara, A., and Wehrhahn, C. (2017). Posterior convergence rate of a class of Dirichlet process mixture model for compositional data. Statistics & Probability Letters, 120:45–51. [Journal]
  28. Barrientos, A. F., Jara, A., and Quintana, F. A. (2015). Bayesian density estimation for compositional data using random Bernstein polynomials. Journal of Statistical Planning and Inference, 166:116–125. [Journal]
  29. González, J., Barrientos, A. F., and Quintana, F. A. (2015). Bayesian nonparametric estimation of test equating functions with covariates. Computational Statistics & Data Analysis, 89:222–244. [Journal]
  30. Barrientos, A. F., Jara, A., and Quintana, F. A. (2012). On the support of MacEachern's dependent Dirichlet processes and extensions. Bayesian Analysis, 7(2):277–310. [Journal]
  31. Barrientos, A. F., Olaya, J., and González, V. (2007). A spline model for electricity demand forecasting. Revista Colombiana de Estadística (Colombian Journal of Statistics), 30(2):187–202. [Journal]

Book Chapters, Conference Proceedings, and Discussions

  1. Bowen, C. M., Snoke, J., Williams, A. R., and Barrientos, A. F. (2026). The case for researching applied privacy-enhancing technologies. In V. J. Hotz, R. Gong, and I. M. Schmutte (Eds.), Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches, and New Findings. University of Chicago Press. [Chapter]
  2. Loyal, J. D. and Barrientos, A. F. (2025). Contributed discussion on "A tree perspective on stick-breaking models in covariate-dependent mixtures" by A. Horiguchi, C. Chan, and L. Ma. Bayesian Analysis, 20(3). [Journal]
  3. Chen, Y., Machanavajjhala, A., Reiter, J. P., and Barrientos, A. F. (2016). Differentially private regression diagnostics. Proceedings of the 2016 IEEE International Conference on Data Mining, 81–90. [Proceedings]
  4. González, J., Barrientos, A. F., and Quintana, F. A. (2015). A dependent Bayesian nonparametric model for test equating. In Quantitative Psychology Research: The 78th Annual Meeting of the Psychometric Society, pages 213–226. Springer International Publishing. [Chapter]

Submitted

  1. Clinch, M., Bradley, J. R., Barrientos, A. F., and Page, G. L. (2026). Bayesian analysis using a constrained mixture of normal-inverse-gamma models. [arXiv]
  2. Page, G. L., Barrientos, A. F., Dahl, D. B., and Dunson, D. B. (2025). Uncertainty quantification in Bayesian clustering. [arXiv]
  3. Liu, R., Wehrhahn, C., Barrientos, A. F., and Jara, A. (2025). Bayesian nonparametric modeling of mixed-type bounded data. [arXiv]
  4. Awan, J., Barrientos, A. F., and Ju, N. (2025). Statistical inference for privatized data with unknown sample size. [arXiv]
  5. Williams, A. R., Barrientos, A. F., Snoke, J., and Bowen, C. M. (2024). Benchmarking differentially private linear regression methods for statistical inference. [NBER]
  6. Timme, D. A., Barrientos, A. F., Chicken, E., and Sinha, D. (2024). Nonparametric multivariate profile monitoring via tree ensembles. [arXiv]