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

In capture-recapture experiments, individual covariates may be subject to missingness, especially when the number of captures is small. When the covariate information is missing at random, the inverse probability weighting method and the multiple imputation method are widely used to obtain point estimators of the abundance. These estimators are then used to construct Wald-type confidence intervals. However, such intervals may have seriously inaccurate coverage probabilities. In this paper, we propose a maximum empirical likelihood (EL) estimation approach for the abundance in the presence of missing covariates. We show that the maximum EL estimator is asymptotically normal, and that the EL ratio statistic for the abundance has a chi-square limiting distribution with one degree of freedom. Simulations indicate that the proposed estimator has a smaller mean square error than existing estimators, and the proposed EL ratio confidence interval usually has more accurate coverage probabilities than the existing Wald-type confidence intervals. We illustrate the proposed method by analyzing data collected in Hong Kong for the yellow-bellied prinia, a bird species.

Automated summary

The paper introduces a maximum empirical likelihood estimation method for estimating abundance in the presence of missing covariates, showing it has smaller mean square error in simulations and more accurate coverage probabilities for confidence intervals than existing methods.