Machine learning estimation of the resident population

Abstract

In this paper, we formulate the problem of estimating the resident population, i.e. correcting for over-counts in administrative register data, as a binary classification problem. We propose a solution based on machine learning algorithms. The selection and the optimisation of the best algorithm is shown to depend on the goal of prediction. We illustrate this method for two important cases of official statistics, Census resident population and survey design with minimum non-response. The performance of the algorithms, the uncertainty of estimates and of the evaluation metrics are described in detail and implemented in shared, open source code. We exemplify with the results obtained by applying this method to Icelandic register and survey data.