Dual system estimation using mixed effects loglinear models

TL;DR

This paper explores the use of mixed effects loglinear models for dual system estimation in official statistics, demonstrating through simulations that they slightly outperform fixed effects models, and extending the approach to multiple systems.

Contribution

It introduces and evaluates mixed effects loglinear models for dual system estimation, offering a scalable alternative to fixed effects models when dealing with many stratifying levels.

Findings

Mixed effects models outperform fixed effects models in simulations.

The improvement in mean squared error is small but consistent.

Extension to multiple system estimation is demonstrated.

Abstract

In official statistics, dual system estimation (DSE) is a well-known tool to estimate the size of a population. Two sources are linked, and the number of units that are missed by both sources is estimated. Often dual system estimation is carried out in each of the levels of a stratifying variable, such as region. DSE can be considered a loglinear independence model, and, with a stratifying variable, a loglinear conditional independence model. The standard approach is to estimate parameters for each level of the stratifying variable. Thus, when the number of levels of the stratifying variable is large, the number of parameters estimated is large as well. Mixed effects loglinear models, where sets of parameters involving the stratifying variable are replaced by a distribution parameterised by its mean and a variance, have also been proposed, and we investigate their properties through simulation. In our simulation studies the mixed effects loglinear model outperforms the fixed effects loglinear model although only to a small extent in terms of mean squared error. We show how mixed effects dual system estimation can be extended to multiple system estimation.