Refereed Journals

1. 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. [Link] [arXiv]
2. 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. [Link]
3. Williams, A. R., Snoke, J., Bowen, C. M., and Barrientos, A. F. (2024b). Disclosing economists’ privacy perspectives: A survey of American Economic Association members on differential privacy and data fitness for use standards. Harvard Data Science Review (In presss). [NBER]
4. Barrientos, A. F., Page, G. L., and Lin, L. (2024). Nonparametric bayesian approach to multiple treatment comparisons in network meta-analysis with application to comparisons of antidepressants. Journal of the Royal Statistical Society Series C: Applied Statistics (In press). [arXiv]
5. Peña, V. and Barrientos, A. F. (2023). Differentially private hypothesis testing with the subsampled and aggregated randomized response mechanism. Statistica Sinica (In press). [Link] [arXiv]
6. Snoke, J., Williams, A. R., Bowen, C. M., and Barrientos, A. F. (2023). Incompatibilities between current practices in statistical data analysis and differential privacy. Journal of Privacy and Confidentiality (In Press). [arXiv]
7. 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. [Link]
8. 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. [Link]
9. Nixon, M. P., Barrientos, A. F., Reiter, J. P., and Slavković, A. (2022). A latent class modeling approach for differentially private synthetic data for contingency tables. Journal of Privacy and Confidentiality, 12(1). [Link]
10. 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. [Link]
11. Barrientos, A. F. and Canale, A. (2021). A Bayesian goodness-of-fit test for regression. Computational Statistics & Data Analysis, 155:107104. [Link]
12. 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. [Link]
13. Barrientos, A. F. and Peña, V. (2020). Bayesian bootstraps for massive data. Bayesian Analysis, 15(2):363-388. [Link]
14. 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. [Link]
15. 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. [Link]
16. 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. [Link]
17. Akande, O., Reiter, J. P., and Barrientos, A. F. (2018). Multiple imputation of missing values in household data with structural zeros. Survey Methodology, 45(2):271–294. [Link]
18. Barrientos, A. F., Bolton, A., Balmat, T., Reiter, J. P., de Figueiredo, J. M., Machanavajjhala, A., Chen, Y., Kneifel, C., 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. [Link]
19. 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. [Link]
20. 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. [Link]
21. 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. [Link]
22. 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. [Link]
23. 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. [Link]
24. Barrientos, A. F., Jara, A., Quintana, F. A. (2012). On the support of MacEachern’s dependent Dirichlet processes and extensions. Bayesian Analysis, 7(2):277–310. [Link]
25. Barrientos, A. F., Olaya, J., and González, V. (2007). A spline model for electricity demand forescasting. Revista Colombiana de Estadística (Colombian Journal of Statistics), 30(2):187–202. [Link]

Refereed Conference Proceedings and Book Chapters

1. Chen, Y., Machanavajjhala, A., Reiter, J. P., and Barrientos, A. F. (2016). Differentially private regression diagnostics. Proceedings of the IEEE International Conference on Data Mining 2016,, ICDM, 81–90. [Link]
2. González, J., Barrientos, A. F., and Quintana, F. A. (2014). 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. [Link]

Submitted

1. Awan, J., Barrientos, A. F., and Ju, N. (2024). Statistical inference for privatized data with unknown sample size. [arXiv]
2. Williams, A. R., Barrientos, A. F., Snoke, J., and Bowen, C. M. (2024a). Benchmarking differentially private linear regression methods for statistical inference. [NBER]
3. Guo, Q., Barrientos, A. F., and Peña, V. (2024). Differentially private methods for compositional data. [arXiv]
4. Iguchi, T., Barrientos, A. F., Chicken, E., and Sinha, D. (2024). Profile monitoring via eigenvector perturbation. [arXiv]
5. Jauch, M., Barrientos, A. F., Peña, V., and Matteson, D. S. (2024). Mixture representations for likelihood ratio ordered distributions. [arXiv]
6. Murphy, J. W., Barrientos, A. F., Andrae, R., Guzman, J., Williams, B. F., Dalcanton, J. J., and Koplitz, B. (2024). The vela pulsar progenitor was most likely a binary merger. [arXiv]