On the deep neutral network-based nonresponse adjustment for complex survey data

Music City Center 

Unit nonresponse is a frequent issue in sample surveys, and naive estimates that do not account for nonrespondents can result in biased outcomes. Common nonresponse adjustment techniques, such as logistic regression and tree-based methods, rely on specific model assumptions that may not hold true, particularly when dealing with highly non-linear and high-dimensional nonresponse mechanisms. In contrast, deep neural network methods have demonstrated effectiveness in managing such complexities. In this paper, we propose the application of deep neural networks for nonresponse adjustment in complex survey data. We compare our approach with established methods, including logistic regression, generalized additive models, and tree-based techniques, through both simulation studies and real-world applications. Our results highlight the advantages of deep neural networks in improving the accuracy of nonresponse adjustments.

Machine learning

Deep learning

Nonresponse

Survey Data