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Open access

Marco Di Zio and Ugo Guarnera

Abstract

The aim of selective editing is to identify observations affected by influential errors. A score function based on the impact of the potential error on target estimates is useful to prioritize observations for accurate reviewing. We assume a Gaussian model for true data and an “intermittent” error mechanism such that a proportion of data is contaminated by an additive Gaussian error. In this setting, scores can be related to the expected value of errors affecting data. Consequently, a set of units can be selected such that the expected residual error in data is below a prefixed threshold. In the context of economic surveys when positive variables are analyzed, the method is more realistically applied to logarithms of data instead of data in their original scale. The method is illustrated through an experimental study on real business survey data where contamination is simulated according to error mechanisms frequently encountered in the practical context of economic surveys.

Open access

Davide Di Cecco, Marco Di Zio, Danila Filipponi and Irene Rocchetti

Abstract

The quantity and quality of administrative information available to National Statistical Institutes have been constantly increasing over the past several years. However, different sources of administrative data are not expected to each have the same population coverage, so that estimating the true population size from the collective set of data poses several methodological challenges that set the problem apart from a classical capture-recapture setting. In this article, we consider two specific aspects of this problem: (1) misclassification of the units, leading to lists with both overcoverage and undercoverage; and (2) lists focusing on a specific subpopulation, leaving a proportion of the population with null probability of being captured. We propose an approach to this problem that employs a class of capturerecapture methods based on Latent Class models. We assess the proposed approach via a simulation study, then apply the method to five sources of empirical data to estimate the number of active local units of Italian enterprises in 2011.