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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
