Huisplattegronden Uit Noordoost-Nederland

Velde, H.M. van der

doi:10.2307/j.ctt2250tkt.9

Cited by 2 publications

(1 citation statement)

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“…To illustrate, we use two registers of Statistics Netherlands, the GBA and the HKS, on people with Afghan, Iranian, or Iraqi (AII) nationality living in the Netherlands in 2007 (shown in Table 1; van der Heijden et al 2012), and on people with a Polish nationality living in the Netherlands in 2009 (shown in Table 1; van der Heijden et al 2011).…”

Section: Two Registers Without Covariatesmentioning

confidence: 99%

Sensitivity of Population Size Estimation for Violating Parametric Assumptions in Log-linear Models

Gerritse¹,

Heijden

Bakker

2015

Journal of Official Statistics

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An important quality aspect of censuses is the degree of coverage of the population. When administrative registers are available undercoverage can be estimated via capture-recapture methodology. The standard approach uses the log-linear model that relies on the assumption that being in the first register is independent of being in the second register. In models using covariates, this assumption of independence is relaxed into independence conditional on covariates. In this article we describe, in a general setting, how sensitivity analyses can be carried out to assess the robustness of the population size estimate. We make use of log-linear Poisson regression using an offset, to simulate departure from the model. This approach can be extended to the case where we have covariates observed in both registers, and to a model with covariates observed in only one register. The robustness of the population size estimate is a function of implied coverage: as implied coverage is low the robustness is low. We conclude that it is important for researchers to investigate and report the estimated robustness of their population size estimate for quality reasons. Extensions are made to log-linear modeling in case of more than two registers and the multiplier method.

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Section: Two Registers Without Covariatesmentioning

confidence: 99%