A new integrated likelihood for estimating population size in dependent dual-record system

Article Type

Research Article

Publication Title

Canadian Journal of Statistics


Efficient estimation of the population size from dependent dual-record system (DRS) remains a statistical challenge in the capture-recapture type experiment. Owing to the non-identifiability of the suitable time-behavioural response variation model (denoted as Mtb) under DRS, few methods are developed in the Bayesian paradigm based on informative priors. Our contribution in this article is to develop a new integrated likelihood function from model Mtb motivated by a novel approach developed by Severini (2007). A suitable weight function on the nuisance parameter is derived with the knowledge of the direction of behavioural dependency. A pseudo-likelihood function is constructed so that the resulting estimator possess some desirable properties including negligible prior (or weight) sensitiveness. Extensive simulations show the superior performance of our proposed method to that of the existing Bayesian methods. Moreover, the proposed estimator is easy to implement from the computational perspective. Applications to two real data sets are presented. The Canadian Journal of Statistics 46: 577–592; 2018 © 2018 Société statistique du Canada.

First Page


Last Page




Publication Date



All Open Access, Green

This document is currently not available here.