A log-linear multidimensional Rasch model for capture-recapture

E Pelle; D J Hessen; P G M van der Heijden

doi:10.1002/sim.6741

A log-linear multidimensional Rasch model for capture-recapture

Stat Med. 2016 Feb 20;35(4):622-34. doi: 10.1002/sim.6741. Epub 2015 Sep 30.

Authors

E Pelle¹, D J Hessen², P G M van der Heijden^{2

3}

Affiliations

¹ Department of Economics, Statistics and Finance, University of Calabria, Arcavacata di Rende, CS, Italy.
² Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands.
³ S3RI, University of Southampton, Southampton, U.K.

PMID: 26423044
DOI: 10.1002/sim.6741

Abstract

In this paper, a log-linear multidimensional Rasch model is proposed for capture-recapture analysis of registration data. In the model, heterogeneity of capture probabilities is taken into account, and registrations are viewed as dichotomously scored indicators of one or more latent variables that can account for correlations among registrations. It is shown how the probability of a generic capture profile is expressed under the log-linear multidimensional Rasch model and how the parameters of the traditional log-linear model are derived from those of the log-linear multidimensional Rasch model. Finally, an application of the model to neural tube defects data is presented.

Keywords: EM algorithm; Rasch model; capture-recapture; heterogeneity; log-linear model; measurement invariance.

MeSH terms

Algorithms
Epidemiologic Methods
Humans
Infant, Newborn
Models, Statistical*
Netherlands / epidemiology
Neural Tube Defects / epidemiology*
Population Surveillance
Probability