Work

Bias Correction in Multiple Systems Estimation

Year: 2025

Type: article

Abstract: If part of a population is hidden but two or more samples are available that each cover parts of this population, multiple systems estimation can be applied to estimate the size of this population. A problem is that these estimates suffer from finite-sample bias that can be substantial in case of a small sample or a small population size. This problem was recognized by Chapman, who derived his essentially unbiased Chapman-estimator for two samples. Because more than two samples may be required to correct for sample dependence, we propose a Generalized Chapman-estimator that can be applied with any number of samples. In a Monte Carlo experiment, this new estimator shows hardly any bias and has smaller standard errors than competing bias-reduced estimators. It is also compared to the usual maximum likelihood estimates for the case of estimating the number of homeless people in the Netherlands, where it shows notably different outcomes.

Source: Journal of Official Statistics

DOI: 10.1177/0282423x251314294

Authors: Daan B. Zult, Peter G. M. van der Heijden, Bart F. M. Bakker

Institutions: Centraal Bureau voor de Statistiek, Utrecht University, University of Southampton, Vrije Universiteit Amsterdam

Language: English

Topic: Census and Population Estimation

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4407829028

FWCI: 0.836

Cites: 37

Cited by: 1

Related to: 10