An empirical comparison of multiple imputation methods for categorical data O Akande, F Li, J Reiter The American Statistician 71 (2), 162-170, 2017 | 102 | 2017 |
Quantitative structure–activity relationship (QSAR) study predicts small-molecule binding to RNA structure Z Cai, M Zafferani, OM Akande, AE Hargrove Journal of medicinal chemistry 65 (10), 7262-7277, 2022 | 39 | 2022 |
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison Z Wang, O Akande, J Poulos, F Li arXiv preprint arXiv:2103.09316, 2021 | 20 | 2021 |
Are deep learning models superior for missing data imputation in surveys? Evidence from an empirical comparison Z Wang, O Akande, J Poulos, F Li Survey Methodology 48 (2), 375-399, 2022 | 12 | 2022 |
A comparative study of imputation methods for multivariate ordinal data C Wongkamthong, O Akande Journal of Survey Statistics and Methodology 11 (1), 189-212, 2023 | 10 | 2023 |
Multiple Imputation and Synthetic Data Generation with NPBayesImputeCat. J Hu, O Akande, Q Wang R Journal 13 (2), 2021 | 9 | 2021 |
Leveraging auxiliary information on marginal distributions in nonignorable models for item and unit nonresponse O Akande, G Madson, DS Hillygus, JP Reiter Journal of the Royal Statistical Society Series A: Statistics in Society 184 …, 2021 | 9 | 2021 |
Multiple imputations for nonignorable item nonresponse in complex surveys using auxiliary margins O Akande, JP Reiter Statistics in the Public Interest: In Memory of Stephen E. Fienberg, 289-306, 2021 | 8 | 2021 |
Multiple imputation of missing values in household data with structural zeros O Akande, J Reiter, AF Barrientos arXiv preprint arXiv:1707.05916, 2017 | 8 | 2017 |
Simultaneous Edit and Imputation For Household Data with Structural Zeros O Akande, A Barrientos, JP Reiter arXiv preprint arXiv:1804.05144, 2018 | 7 | 2018 |
Analyzing pace-of-play in soccer using spatio-temporal event data E Shen, S Santo, O Akande Journal of Sports Analytics 8 (2), 127-139, 2022 | 6 | 2022 |
Are deep learning models superior for missing data imputation in large surveys Z Wang, O Akande, J Poulos, F Li Evidence from an empirical comparison. arXiv preprint arXiv 210309316, 2021 | 6 | 2021 |
Bayesian models for imputing missing data and editing erroneous responses in surveys OM Akande Duke University, 2019 | 3 | 2019 |
Are Deep Learning Models Superior for Missing Data Imputation in Large Surveys? Evidence from an Empirical Comparison. arXiv 2022 Z Wang, O Akande, J Poulos, F Li arXiv preprint arXiv:2103.09316, 0 | 2 | |
Multiple Imputation and Synthetic Data Generation with the R package NPBayesImputeCat J Hu, O Akande, Q Wang arXiv preprint arXiv:2007.06101, 2020 | 1 | 2020 |
Supplementary material for “Are deep learning models su-perior for missing data imputation in large surveys? Evi-dence from an empirical comparison” Z Wang, O Akande, J Poulos, F Li | | |