One of the most important task for epidemiologists, biologists, ecologists and sociologists is to analyse and forecast possible changes and dynamics in a population. Capture-recapture experiments may be used to obtain meaningful information from population under study. The rational behind this method is to account for unobserved individuals by using observed individual trapping histories. A central assumption in traditional capture-recapture approach is the homogeneity of the capture probability. However, differences of character or behaviour between individuals may occur and this fact results in indirect dependence between registrations. Psychometric models, such as the Rasch model, may be successfully applied. We propose the use of the multidimensional Rasch model in the capture-recapture context. In particular, we assume that registrations may be divided into two or more subgroups, such that they can be view as indicators of the latent variables which account for correlations among registrations. To do so, the extension of the Dutch Identity for the multidimensional partial credit model can be utilized. It allows us to express the multidimensional Rasch model in a log-linear representation and to derive the parameters of the traditional log-linear model from those of the multidimensional Rasch model.

(2014). Log-linear multidimensional Rasch model for capture-recapture. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2014).

Log-linear multidimensional Rasch model for capture-recapture

PELLE, ELVIRA
2014

Abstract

One of the most important task for epidemiologists, biologists, ecologists and sociologists is to analyse and forecast possible changes and dynamics in a population. Capture-recapture experiments may be used to obtain meaningful information from population under study. The rational behind this method is to account for unobserved individuals by using observed individual trapping histories. A central assumption in traditional capture-recapture approach is the homogeneity of the capture probability. However, differences of character or behaviour between individuals may occur and this fact results in indirect dependence between registrations. Psychometric models, such as the Rasch model, may be successfully applied. We propose the use of the multidimensional Rasch model in the capture-recapture context. In particular, we assume that registrations may be divided into two or more subgroups, such that they can be view as indicators of the latent variables which account for correlations among registrations. To do so, the extension of the Dutch Identity for the multidimensional partial credit model can be utilized. It allows us to express the multidimensional Rasch model in a log-linear representation and to derive the parameters of the traditional log-linear model from those of the multidimensional Rasch model.
PERRI, PIER FRANCESCO
Rasch model, capture-recapture, heterogeneity, log-linear model, EM algorithm
SECS-S/01 - STATISTICA
English
22-mag-2014
Scuola di Dottorato in Statistica e Matematica Applicata alla Finanza
STATISTICA ED APPLICAZIONI - 62R
26
2012/2013
Nel periodo febbraio-dicembre 2013 il lavoro di tesi è stato svolto presso il "Department of Methodology and Statistics, Utrecht University (The Netherlands)", dove ho collaborato con i Professori Peter G.M. van der Heijden e Dave Hessen.
open
(2014). Log-linear multidimensional Rasch model for capture-recapture. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2014).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/52008
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