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Maximum likelihood abundance estimation from capture-recapture data when covariates are missing at random.

Yang Liu1, Yukun Liu1, Pengfei Li2

  • 1KLATASDS - MOE, School of Statistics, East China Normal University, Shanghai, China.

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|July 17, 2020
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Summary
This summary is machine-generated.

This study introduces a new maximum empirical likelihood (EL) method for estimating population abundance in capture-recapture studies with missing covariate data. The proposed method offers improved accuracy and reliability for abundance estimation and confidence intervals.

Keywords:
abundancecapture-recapture data analysisempirical likelihoodmissing at random

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Area of Science:

  • Ecology
  • Wildlife population estimation
  • Statistical modeling

Background:

  • Capture-recapture studies are vital for estimating wildlife abundance.
  • Missing covariate data can compromise the accuracy of traditional estimation methods.
  • Existing methods like inverse probability weighting and multiple imputation may yield inaccurate confidence intervals.

Purpose of the Study:

  • To develop a robust statistical method for estimating population abundance in the presence of missing covariate data.
  • To improve the accuracy of confidence intervals for abundance estimates.
  • To address limitations of existing methods in capture-recapture experiments.

Main Methods:

  • Proposed a maximum empirical likelihood (EL) estimation approach for abundance.
  • Investigated the asymptotic properties of the maximum EL estimator.
  • Developed an EL ratio statistic for hypothesis testing and confidence interval construction.
  • Conducted simulation studies to compare performance with existing methods.

Main Results:

  • The maximum EL estimator is asymptotically normal.
  • The EL ratio statistic follows a chi-square distribution.
  • Simulations showed the EL method has a smaller mean square error than existing estimators.
  • The EL ratio confidence intervals demonstrated more accurate coverage probabilities.

Conclusions:

  • The maximum empirical likelihood method provides a more accurate and reliable approach for estimating population abundance with missing covariate data.
  • The proposed EL ratio confidence intervals offer improved coverage accuracy compared to Wald-type intervals.
  • This method enhances the precision of ecological assessments in capture-recapture studies.