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Classical multilevel and Bayesian approaches to population size estimation using multiple lists
Author(s) -
Fienberg S. E.,
Johnson M. S.,
Junker B. W.
Publication year - 1999
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/1467-985x.00143
Subject(s) - rasch model , contingency table , bayesian probability , random effects model , log linear model , computer science , population , multilevel model , context (archaeology) , econometrics , linear model , bayes' theorem , hierarchical database model , generalized linear mixed model , statistics , machine learning , mathematics , data mining , artificial intelligence , geography , medicine , meta analysis , archaeology , demography , sociology
One of the major objections to the standard multiple‐recapture approach to population estimation is the assumption of homogeneity of individual ‘capture’ probabilities. Modelling individual capture heterogeneity is complicated by the fact that it shows up as a restricted form of interaction among lists in the contingency table cross‐classifying list memberships for all individuals. Traditional log‐linear modelling approaches to capture–recapture problems are well suited to modelling interactions among lists but ignore the special dependence structure that individual heterogeneity induces. A random‐effects approach, based on the Rasch model from educational testing and introduced in this context by Darroch and co‐workers and Agresti, provides one way to introduce the dependence resulting from heterogeneity into the log‐linear model; however, previous efforts to combine the Rasch‐like heterogeneity terms additively with the usual log‐linear interaction terms suggest that a more flexible approach is required. In this paper we consider both classical multilevel approaches and fully Bayesian hierarchical approaches to modelling individual heterogeneity and list interactions. Our framework encompasses both the traditional log‐linear approach and various elements from the full Rasch model. We compare these approaches on two examples, the first arising from an epidemiological study of a population of diabetics in Italy, and the second a study intended to assess the ‘size’ of the World Wide Web. We also explore extensions allowing for interactions between the Rasch and log‐linear portions of the models in both the classical and the Bayesian contexts.