Estimating the size of a closed population by modeling latent and observed heterogeneity

by   Antonio Forcina, et al.

The paper describe a new class of capture-recapture models for closed populations when individual covariates are available. The novelty consists in combining a latent class model for capture probabilities where the marginal weights and the conditional distributions given the latent may depend on covariates, with a model for the marginal distribution of the available covariates. In addition, a general formulation for the conditional distributions given the latent and covariates which allows serial dependence is provided. A Fisher scoring algorithm for maximum likelihood estimation is presented, asymptotic results are derived, and a procedure for constructing likelihood based confidence intervals for the population total is presented. Two examples with real data are used to illustrate the proposed approach.



There are no comments yet.


page 1

page 2

page 3

page 4


Bivariate Discrete Inverse Weibull Distribution

In this paper, we propose a new class of bivariate distributions, called...

Grouped Heterogeneous Mixture Modeling for Clustered Data

Clustered data which has a grouping structure (e.g. postal area, school,...

Regression-based Network Reconstruction with Nodal and Dyadic Covariates and Random Effects

Network (or matrix) reconstruction is a general problem which occurs if ...

Modeling Excess Deaths After a Natural Disaster with Application to Hurricane Maria

Estimation of excess deaths due to a natural disaster is an important pu...

A Flexible Quasi-Copula Distribution for Statistical Modeling

Copulas, generalized estimating equations, and generalized linear mixed ...

drpop: Efficient and Doubly Robust Population Size Estimation in R

This paper introduces the R package drpop to flexibly estimate total pop...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.