Capture-Recapture Methods for the Social and Medical Sciences

1. Auflage 2017
ISBN: 978-1-4987-4531-4
Verlag: CRC Press

Buch, Englisch, 462 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 1021 g

Reihe: Chapman & Hall/CRC Interdisciplinary Statistics

Capture-recapture methods have been used in biology and ecology for more than 100 years. However, it is only recently that these methods have become popular in the social and medical sciences to estimate the size of elusive populations such as illegal immigrants, illicit drug users, or people with a drinking problem. Capture-Recapture Methods for the Social and Medical Sciences brings together important developments which allow the application of these methods. It has contributions from more than 40 researchers, and is divided into eight parts, including topics such as ratio regression models, capture-recapture meta-analysis, extensions of single and multiple source models, latent variable models and Bayesian approaches.

The book is suitable for everyone who is interested in applying capture-recapture methods in the social and medical sciences. Furthermore, it is also of interest to those working with capture-recapture methods in biology and ecology, as there are some important developments covered in the book that also apply to these classical application areas.

Autoren/Hrsg.

Bohning, Dankmar

van der Heijden, Peter G.M.

Bunge, John

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Psychiatrie, Sozialpsychiatrie, Suchttherapie

Inhaltsverzeichnis

I Introductory Part

Basic concepts of capture-recapture

II Ratio Regression Models

Ratio regression and capture-recapture

The Conway-Maxwell-Poisson distribution and capture-recapture count data

The geometric distribution, the ratio plot under the null and the burden of Dengue Fever in Chiang Mai province

A ratio regression approach to estimate the size of the Salmonella infected flock population using validation information

III Meta-Analysis in Capture–Recapture

On meta-analysis in capture-recapture

A case study on maritime accidents using meta-analysis in capture-recapture

A meta-analytic generalization of the Lincoln-Petersen-estimator for mark-and-resight studies

IV Extensions of Single Source Models

Estimating the population size via the empirical probability generating function

Convex estimation

Non-parametric estimation of the population size using the empirical probability generating function

Extending the truncated Poisson regression model to a time-at-risk model

Extensions of the Chao-estimator for covariate information: Poisson case

Population size estimation for one-inflated count data based upon the geometric distribution

V Multiple Sources

Dual and multiple system estimation: fully observed and incomplete covariates

Population size estimation in CRC Models with continuous covariates

Trimmed dual system estimation

Estimation of non-registered usual residents in the Netherlands

VI Latent Variable Models

Population size estimation using a categorical latent variable

Latent class - Rasch models and marginal extensions

Performance of hierarchical log-linear models for a heterogeneous population with three lists

A multidimensional Rasch model for multiple system estimation where the number of lists changes over time

Extending the Lincoln-Petersen estimator when both sources are counts

VII Bayesian Approaches

Objective Bayes estimation of the population size using Kemp distributions

Bayesian population size estimation with censored counts

VIII Miscellaneous Topics

Uncertainty assessment in capture-recapture studies and the choice of sampling effort

Über Autor(innen)

Dankmar Böhning is Professor of Medical Statistics and Director of the Southampton Statistical Sciences Research Institute at the University of Southampton. His interests are in capture-recapture modelling, meta-analysis and research synthesis as well as mixed modelling.

John Bunge is Professor of Statistics in the Department of Statistical Science of Cornell University. His interests are capture-recapture modelling, microbiome statistics, and nonclassical probability distribution theory.

Peter. G.M. van der Heijden is Professor of Social Statistics at the University of Utrecht and at the University of Southampton. His interests are capture-recapture modelling for the Social Sciences and Official Statistics.