Publications

Wongkamthong, C., and Akande, O. (forthcoming), “A Comparative Study of Imputation Methods for Multivariate Ordinal Data,” Journal of Survey Statistics and Methodology. [arXiv Link]
Akande, O. and Reiter, J. P. (forthcoming), “Multiple Imputation for Nonignorable Item Nonresponse in Complex Surveys Using Auxiliary Margins,” Statistics in the Public Interest–In Memory of Stephen E. Fienberg, edited by A. Carriquiry, W. Eddy, and J. Tanur, Springer. [arXiv Link]
Akande, O., Madson, G., Hillygus, D. S. and Reiter, J. P. (2021), “Leveraging Auxiliary Information on Marginal Distributions in Nonignorable Models for Item and Unit Nonresponse in Surveys”, Journal of the Royal Statistical Society A.
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.
Akande, O., Barrientos, A. F. and Reiter, J. P. (2018), “Simultaneous Edit and Imputation For Household Data with Structural Zeros”, Journal of Survey Statistics and Methodology, 7:4, 498–519.
Akande, O., Li, F. and Reiter, J. P. (2017), “An Empirical Comparison of Multiple Imputation Methods for Categorical Data”, The American Statistician, 71:2, 162-170.

Hu, J., Akande, O., and Wang, Q., “Multiple Imputation and Synthetic Data Generation with the R package NPBayesImputeCat,” submitted. [arXiv Link]
Wang, Z., Akande, O., Poulos, J., Li F., “Are deep learning models superior for missing data imputation in complex surveys?: Evidence from an empirical comparison,” submitted.

Wongkamthong, C., and Akande, O., “Imputing survey responses on political ideology using a hierarchical Dirichlet process mixture of multinomial distributions.”
Akande, O., Hu, J., Wang, Q., and Reiter, J. P., “Edit-Imputation and Synthetic Data Generation For Household Data with the R package NestedCategBayesImpute.”