Generalized linear models and Capture-Recapture Method in a closed population: strengths and weaknesses Capture-recapture methods are used by epidemiologists in order to estimate the size of hidden populations using incomplete and overlapping lists of cases. These models can be both continuous and discrete time and the particular population we want to obtain a quantitative evaluation can be assumed to be closed or open. Here we specifically consider discrete-time models for closed population. The problem was treated using Generalized Linear Models as they allow to treat simultaneously both forms of dependence between sources than observed heterogeneity due to covariates effects. Specifically, we analyzed the strengths and weaknesses of Multinomial Conditional Logistic Model and presented a comparison with a correspondent Bayesian approach. The estimates obtained on simulated and real data appear to be enough reliable.

GENERALIZED LINEAR MODELS AND CAPTURE-RECAPTURE METHOD IN A CLOSED POPULATION: STRENGTHS AND WEAKNESSES / G.Rossi; P.Pepe; O.Curzio; M.Marchi. - In: STATISTICA. - ISSN 0390-590X. - STAMPA. - LXX, n°3:(2010), pp. 371-390.

GENERALIZED LINEAR MODELS AND CAPTURE-RECAPTURE METHOD IN A CLOSED POPULATION: STRENGTHS AND WEAKNESSES

MARCHI, MARCO
2010

Abstract

Generalized linear models and Capture-Recapture Method in a closed population: strengths and weaknesses Capture-recapture methods are used by epidemiologists in order to estimate the size of hidden populations using incomplete and overlapping lists of cases. These models can be both continuous and discrete time and the particular population we want to obtain a quantitative evaluation can be assumed to be closed or open. Here we specifically consider discrete-time models for closed population. The problem was treated using Generalized Linear Models as they allow to treat simultaneously both forms of dependence between sources than observed heterogeneity due to covariates effects. Specifically, we analyzed the strengths and weaknesses of Multinomial Conditional Logistic Model and presented a comparison with a correspondent Bayesian approach. The estimates obtained on simulated and real data appear to be enough reliable.
LXX, n°3
371
390
G.Rossi; P.Pepe; O.Curzio; M.Marchi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/417454
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