The traditional capture-recapture method assumes homogeneity of the capture probabilities. However, dierences of character or behaviour between individuals may occur and models that allow for varying susceptibility to capture over individuals and unequal catchability have been proposed and psychometric models, such as the Rasch model, were successfully applied. In the present work, we propose the use of the multidimensional Rasch model in the capture-recapture context. We assume that lists may be divided into two or more subgroups, such that they can be viewed as indicators of the latent variables which account for correlations among lists. We show how to express the probability of a generic capture prole in terms of log-linear multidimensional Rasch model and apply the methodology to a real data set.
Log-linear multidimensional Rasch model for capture-recapture / Pelle, Elvira; Hessen, David J.; Peter G. M., Van der Heijden. - (2014), pp. 435-442. (Intervento presentato al convegno 21st International Conference on Computational Statistics (COMPSTAT 2014) tenutosi a Ginevra nel 19-22 agosto, 2014).
Log-linear multidimensional Rasch model for capture-recapture
Pelle, Elvira;
2014
Abstract
The traditional capture-recapture method assumes homogeneity of the capture probabilities. However, dierences of character or behaviour between individuals may occur and models that allow for varying susceptibility to capture over individuals and unequal catchability have been proposed and psychometric models, such as the Rasch model, were successfully applied. In the present work, we propose the use of the multidimensional Rasch model in the capture-recapture context. We assume that lists may be divided into two or more subgroups, such that they can be viewed as indicators of the latent variables which account for correlations among lists. We show how to express the probability of a generic capture prole in terms of log-linear multidimensional Rasch model and apply the methodology to a real data set.File | Dimensione | Formato | |
---|---|---|---|
Pubblicazione 11.pdf
Accesso riservato
Descrizione: Articolo principale
Tipologia:
Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione
239.32 kB
Formato
Adobe PDF
|
239.32 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris