We extend the approach by Rocchetti et al. (2011) to situations where the number of sampling occasions is known and fixed. We propose an estimator based on a novel weighted regression model for (log) ratios of successive capture frequencies. The proposed estimator prevents identifiability and boundary problems which are quite standard in ML estimation. The observed frequency data are used to provide estimates for the number of unregistered individuals. The results from simulation and three benchmark datasets are encouraging.

A regression estimator for mixed binomial capture-recapture data / Rocchetti, Irene; Alfo', Marco; D., Bohening. - In: JOURNAL OF STATISTICAL PLANNING AND INFERENCE. - ISSN 0378-3758. - STAMPA. - 145:(2014), pp. 165-178. [10.1016/j.jspi.2013.08.010]

A regression estimator for mixed binomial capture-recapture data

ROCCHETTI, IRENE;ALFO', Marco;
2014

Abstract

We extend the approach by Rocchetti et al. (2011) to situations where the number of sampling occasions is known and fixed. We propose an estimator based on a novel weighted regression model for (log) ratios of successive capture frequencies. The proposed estimator prevents identifiability and boundary problems which are quite standard in ML estimation. The observed frequency data are used to provide estimates for the number of unregistered individuals. The results from simulation and three benchmark datasets are encouraging.
2014
beta-binomial; weighted regression; zero-truncation
01 Pubblicazione su rivista::01a Articolo in rivista
A regression estimator for mixed binomial capture-recapture data / Rocchetti, Irene; Alfo', Marco; D., Bohening. - In: JOURNAL OF STATISTICAL PLANNING AND INFERENCE. - ISSN 0378-3758. - STAMPA. - 145:(2014), pp. 165-178. [10.1016/j.jspi.2013.08.010]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/549310
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