66 citations · 94 across the 8 of their papers we have counts for
8 papers
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison
Zhenhua Wang, Olanrewaju Akande, Jason Poulos +1
Multiple imputation (MI) is a popular approach for dealing with missing data arising from non-response in sample surveys. Multiple imputation by chained equations (MICE) is one of…
Multiple Imputation for Nonignorable Item Nonresponse in Complex Surveys Using Auxiliary Margin
Olanrewaju Akande, Jerome P. Reiter
We outline a framework for multiple imputation of nonignorable item nonresponse when the marginal distributions of some of the variables with missing values are known. In particula…
A Comparative Study of Imputation Methods for Multivariate Ordinal Data
Chayut Wongkamthong, Olanrewaju Akande
Missing data remains a very common problem in large datasets, including survey and census data containing many ordinal responses, such as political polls and opinion surveys. Multi…
Multiple Imputation and Synthetic Data Generation with the R package NPBayesImputeCat
Jingchen Hu, Olanrewaju Akande, Quanli Wang
In many contexts, missing data and disclosure control are ubiquitous and challenging issues. In particular at statistical agencies, the respondent-level data they collect from surv…
Leveraging Auxiliary Information on Marginal Distributions in Nonignorable Models for Item and Unit Nonresponse
Olanrewaju Akande, Gabriel Madson, D. Sunshine Hillygus +1
Often, government agencies and survey organizations know the population counts or percentages for some of the variables in a survey. These may be available from auxiliary sources,…
Simultaneous Edit and Imputation for Household Data with Structural Zeros
Olanrewaju Akande, Andrés Barrientos, Jerome P. Reiter
Multivariate categorical data nested within households often include reported values that fail edit constraints---for example, a participating household reports a child's age as ol…
Multiple Imputation of Missing Values in Household Data with Structural Zeros
Olanrewaju Akande, Jerome Reiter, Andrés F. Barrientos
We present an approach for imputation of missing items in multivariate categorical data nested within households. The approach relies on a latent class model that (i) allows for ho…
An Empirical Comparison of Multiple Imputation Methods for Categorical Data
Olanrewaju Akande, Fan Li, Jerome Reiter
Multiple imputation is a common approach for dealing with missing values in statistical databases. The imputer fills in missing values with draws from predictive models estimated f…