| Disclosure risk evaluation for fully synthetic categorical data J Hu, JP Reiter, Q Wang International Conference on Privacy in Statistical Databases, 185-199, 2014 | 72 | 2014 |
| Probability and Bayesian Modeling J Albert, J Hu Chapman and Hall/CRC, 2019 | 63 | 2019 |
| Dirichlet process mixture models for modeling and generating synthetic versions of nested categorical data J Hu, JP Reiter, Q Wang Bayesian Analysis 13 (1), 183-200, 2018 | 60 | 2018 |
| Bayesian estimation of attribute and identification disclosure risks in synthetic data J Hu Transactions of Data Privacy 12 (1), 61-89, 2019 | 49 | 2019 |
| Bayesian non‐parametric generation of fully synthetic multivariate categorical data in the presence of structural zeros D Manrique‐Vallier, J Hu Journal of the Royal Statistical Society: Series A (Statistics in Society …, 2018 | 27 | 2018 |
| Synthesizing geocodes to facilitate access to detailed geographical information in large scale administrative data J Drechsler, J Hu Journal of Survey Statistics and Methodology 9 (3), 523-548, 2021 | 26 | 2021 |
| Bayesian pseudo posterior mechanism under asymptotic differential privacy TD Savitsky, MR Williams, J Hu Journal of Machine Learning Research 23 (55), 1-37, 2022 | 18* | 2022 |
| A Bayesian Statistics Course for Undergraduates: Bayesian Thinking, Computing, and Research J Hu Journal of Statistics Education 28 (3), 229-235, 2020 | 17 | 2020 |
| Identification Risks Evaluation of Partially Synthetic Data with the IdentificationRiskCalculation R Package R Hornby, J Hu Transactions of Data Privacy 14 (1), 37-52, 2021 | 16* | 2021 |
| Risk-efficient Bayesian pseudo posterior data synthesis for privacy protection J Hu, TD Savitsky, MR Williams Journal of Survey Statistics and Methodology 10 (5), 1370-1399, 2022 | 15 | 2022 |
| The current state of undergraduate Bayesian education and recommendations for the future M Dogucu, J Hu The American Statistician 76 (4), 405-413, 2022 | 14 | 2022 |
| The Quasi-Multinomial Synthesizer for Categorical Data J Hu, N Hoshino International Conference on Privacy in Statistical Databases, 75-91, 2018 | 14 | 2018 |
| Bayesian Data Synthesis and Disclosure Risk Quantification: An Application to the Consumer Expenditure Surveys J Hu, TD Savitsky Transactions of Data Privacy 16 (2), 83-121, 2023 | 13* | 2023 |
| Private tabular survey data products through synthetic microdata generation J Hu, TD Savitsky, MR Williams Journal of Survey Statistics and Methodology 10 (3), 720-752, 2022 | 13 | 2022 |
| Multiple imputation and synthetic data generation with the R package NPBayesImputeCat J Hu, O Akande, Q Wang The R Journal 13 (2), 90-110, 2021 | 12* | 2021 |
| Bayesian Computing in the Undergraduate Statistics Curriculum J Albert, J Hu Journal of Statistics Education 28 (3), 236-247, 2020 | 12 | 2020 |
| Teaching an Undergraduate Course in Bayesian Statistics: A Panel Discussion A Johnson, C Rundel, J Hu, K Ross, A Rossman Journal of Statistics Education 28 (3), 251-261, 2020 | 11 | 2020 |
| Advancing microdata privacy protection: A review of synthetic data methods J Hu, CMK Bowen Wiley Interdisciplinary Reviews: Computational Statistics 16 (1), e1636, 2024 | 8 | 2024 |
| Are independent parameter draws necessary for multiple imputation? J Hu, R Mitra, J Reiter The American Statistician 67 (3), 143-149, 2013 | 8 | 2013 |
| NPBayesImpute: Non-Parametric Bayesian Multiple Imputation for Categorical Data Q Wang, D Manrique-Vallier, JP Reiter, J Hu R package version 0.6, 2016 | 7 | 2016 |
Quanli WangAstraZenecaDirección de correo verificada de astrazeneca.com
