<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Population Size Estimation Using Multiple Incomplete Lists with Overcoverage

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Population Size Estimation Using Multiple Incomplete Lists with Overcoverage"}

The quantity and quality of administrative information available to National Statistical Institutes have been constantly increasing over the past several...

Population Size Estimation Using Multiple Incomplete Lists with Overcoverage

Online veröffentlicht: 07. Juni 2018

Seitenbereich: 557 - 572

Eingereicht: 01. Okt. 2016

Akzeptiert: 01. Juli 2017

DOI: https://doi.org/10.2478/jos-2018-0026

Schlüsselwörter
Multisource integration, capture-recapture models, latent class models, missing data

© 2018 Davide Di Cecco et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Population Size Estimation Using Multiple Incomplete Lists with Overcoverage

Online veröffentlicht: 07. Juni 2018

Seitenbereich: 557 - 572

Eingereicht: 01. Okt. 2016

Akzeptiert: 01. Juli 2017

DOI: https://doi.org/10.2478/jos-2018-0026

SchlüsselwörterMultisource integration, capture-recapture models, latent class models, missing data

© 2018 Davide Di Cecco et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Multisource integration, capture-recapture models, latent class models, missing data