Estimation in closed capture–recapture models when covariates are missing at random

DOI10.1111/BIOM.12498zbMath1390.62280OpenAlexW2277977846WikidataQ39973067 ScholiaQ39973067MaRDI QIDQ5355255

Wen-Han Hwang, Jean de Dieu Tapsoba, Shen-Ming Lee

Publication date: 7 September 2017

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.12498

zbMATH Keywords

multiple imputationmissing at randomregression calibrationinverse probability weightingpopulation size estimation

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) General nonlinear regression (62J02)

Related Items (5)

Validation likelihood estimation method for a zero-inflated Bernoulli regression model with missing covariates ⋮ Maximum likelihood abundance estimation from capture‐recapture data when covariates are missing at random ⋮ Estimation of logistic regression with covariates missing separately or simultaneously via multiple imputation methods ⋮ Abundance estimation based on optimal estimating function with missing covariates in capture-recapture studies ⋮ Estimation of a zero-inflated Poisson regression model with missing covariates via nonparametric multiple imputation methods

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