# Closed-population capture--recapture models with measurement error and missing observations in covariates

@article{Stoklosa2019ClosedpopulationCM, title={Closed-population capture--recapture models with measurement error and missing observations in covariates}, author={Jakub Stoklosa and Shen‐Ming Lee and Wen-Han Hwang}, journal={Statistica Sinica}, year={2019} }

In capture–recapture experiments, covariates collected on individuals, such as body weight and length, are often measured imprecisely or are missing at random. Furthermore, the number of recorded covariate measurements collected on each observed individual is usually equal to or less than the individual’s capture frequency. Correcting for multiple error-prone covariate is seldom seen in capture–recapture models and even fewer research have considered cases where individual’s have no…

## 4 Citations

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

- MathematicsBiometrics
- 2020

Simulations indicate that the proposed maximum empirical likelihood (EL) estimation approach for the abundance in the presence of missing covariates 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.

### Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables

- Computer ScienceComput. Stat.
- 2020

This paper considers likelihood-based multiple imputation to handle missing data, and combines this with two well-known functional measurement error methods: simulation-extrapolation and corrected score to substantially reduce bias and mean squared errors in regression coefficients.

### Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables

- Computer ScienceComputational Statistics
- 2020

This paper considers likelihood-based multiple imputation to handle missing data, and combines this with two well-known functional measurement error methods: simulation-extrapolation and corrected score to substantially reduce bias and mean squared errors in regression coefficients.

### Semiparametric empirical likelihood inference for abundance from one‐inflated capture–recapture data

- MathematicsBiometrical journal. Biometrische Zeitschrift
- 2022

Abundance estimation from capture–recapture data is of great importance in many disciplines. Analysis of capture–recapture data is often complicated by the existence of one‐inflation and…

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