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

Estimation of growth parameters from multiple-recapture data.

You-Gan Wang1

  • 1Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546. stawyg@nus.edu.sg

Biometrics
|September 2, 2004
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

Colorimetric and luminescent detection of cysteamine based on lanthanide-doped upconversion nanoparticles and gold nanoparticles.

Mikrochimica acta·2026
Same author

Multi-Granularity Autoformer for long-term deterministic and probabilistic power load forecasting.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Classical and machine learning tools for identifying yellow-seeded <i>Brassica napus</i> by fusion of hyperspectral features.

Frontiers in genetics·2025
Same author

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China.

Heliyon·2024
Same author

Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model.

Water research·2024
Same author

Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework.

BMC bioinformatics·2023

This study introduces a new method for analyzing animal growth data with unknown initial ages and multiple recaptures. The approach treats lengths as repeated measures, offering a verifiable alternative to existing imputation or random effects models.

Area of Science:

  • Ecology
  • Population Dynamics
  • Quantitative Biology

Background:

  • Analyzing animal growth data is crucial for ecological and fisheries management.
  • Existing methods for growth analysis with unknown initial ages have limitations, often relying on unverifiable assumptions.
  • Accurate growth parameter estimation is essential for understanding population dynamics.

Purpose of the Study:

  • To develop a novel statistical method for analyzing growth data with multiple recaptures when initial ages are unknown.
  • To provide a robust and verifiable approach that avoids assumptions about initial ages.
  • To offer a computationally efficient method for estimating growth parameters and variance components.

Main Methods:

  • The proposed method treats all recorded lengths, including the first capture, as correlated repeated measures for each individual.

Related Experiment Videos

  • Generalized estimating equations (GEE) are employed to develop optimal estimating equations.
  • The method requires only the first two moment assumptions, enhancing its applicability.

    • Main Results:

    • Explicit expressions for estimating mean growth parameters and variance components are derived.
    • Simulation studies demonstrate the effectiveness and reliability of the proposed method.
    • The approach successfully analyzes real-world data from whelks and southern rock lobsters.

    Conclusions:

    • The developed method provides a significant advancement in analyzing growth data with unknown initial ages.
    • This approach offers a more robust and verifiable alternative to traditional methods.
    • The findings have practical implications for fisheries science and population modeling.