Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Logistic regression in capture-recapture models.

J M Alho1

  • 1Department of Statistics, University of Illinois, Urbana 61801.

Biometrics
|September 1, 1990
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Scenarios, uncertainty and conditional forecasts of the world population.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·1997
Same author

Mixed estimation of old-age mortality.

Mathematical population studies·1997
Same author

Interval estimation of inverse dose-response.

Biometrics·1995
Same author

Estimating heterogeneity in the probabilities of enumeration for dual-system estimation.

Journal of the American Statistical Association·1993
Same author

Population forecasting theory, methods and assessments of accuracy. The magnitude of error due to different vital processes in population forecasts.

International journal of forecasting·1992
Same author

On prevalence, incidence, and duration in general stable populations.

Biometrics·1992
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces a new logistic regression model to estimate population size, accounting for varying individual and temporal capture probabilities. The proposed estimator is statistically sound and validated with simulations and real-world occupational disease data.

Area of Science:

  • Biostatistics
  • Population Ecology
  • Epidemiology

Background:

  • Capture-recapture models are widely used for population size estimation.
  • Population heterogeneity, where individuals have different capture probabilities, can bias traditional models.
  • Accurate estimation is crucial for ecological and epidemiological studies.

Purpose of the Study:

  • To develop and evaluate a new capture-recapture model that explicitly accounts for population heterogeneity.
  • To introduce a logistic regression-based estimator for population size under heterogeneity.
  • To assess the statistical properties and practical performance of the proposed estimator.

Main Methods:

  • A logistic regression model was developed to allow for varying capture probabilities across individuals and time.

Related Experiment Videos

  • Conditional maximum likelihood was used to estimate model parameters from observed capture data.
  • The consistency and asymptotic normality of the population size estimator were theoretically derived, and a variance estimator was developed.

    • Main Results:

    • The proposed population size estimator was shown to be consistent and asymptotically normal.
    • A novel variance estimator was derived to account for population heterogeneity.
    • Simulation studies demonstrated favorable finite-sample properties of the estimators.

    Conclusions:

    • The developed logistic regression model effectively addresses population heterogeneity in capture-recapture analyses.
    • The new estimator provides a statistically robust method for population size estimation in complex scenarios.
    • The model's utility was confirmed through an application to Finnish occupational disease registration data.