Neural network imputation: An experience with the national resources inventory survey

Abstract

Imputation is needed in almost all major surveys. Imputation tools are often adopted according to the convenience and the contexts of the surveys. Traditional hot-deck imputation needs extensive knowledge of the survey variables. Explicit model-based imputation needs a valid model for every survey variable. In large-scale national surveys, different groups of people with different backgrounds work on different stages of surveys and often the statistical estimation group has little or insufficient communication with the other groups. In such situations, it is difficult to use hot-deck imputation. On the other hand, because of the complex nature of the survey, finding a suitable model for every survey variable may not be easy and thus a nonparametric method— such as neural network imputation—may be attractive. One such large-scale national survey is the U.S. Department of Agriculture’s National Resources Inventory Survey (NRI). By design, the survey has missing values. The missing values are imputed using a donor-based method. This article develops a neural network imputation model and compares its performance with that of the existing imputation method. The end result looks promising.