Analysis of capture-recapture data with a Rasch-type model allowing for conditional dependence and multidimensionality.
In this article, we show that, if subjects are assumed to be homogeneous within a finite set of latent classes, the basic restrictions of the Rasch model (conditional independence and unidimensionality) can be relaxed in a flexible way by simply adding appropriate columns to a basic design matrix. When discrete covariates are available so that subjects may be classified into strata, we show how a joint modeling approach can achieve greater parsimony. Parameter estimates may be obtained by maximizing the conditional likelihood (given the total number of captures) with a combined use of the EM and Fisher scoring algorithms. We also discuss a technique for obtaining confidence intervals for the size of the population under study based on the profile likelihood.
Bartolucci
F
F
Dipartimento di Scienze Statistiche, Università di Perugia, Italy. bart@stat.unipg.it
Forcina
A
A
D000465:Algorithms; D000818:Animals; D001699:Biometry; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D003920:Diabetes Mellitus / Q000453:epidemiology; D004812:Epidemiologic Methods; D006801:Humans; D007558:Italy / Q000453:epidemiology; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D011156:Population Density