A method of inference for a capture-recapture experiment in discrete time with variable capture probabilities

DOI10.1080/15326349108807195zbMath0737.62094OpenAlexW2081003237MaRDI QIDQ3983731

Paul S. F. Yip

Publication date: 27 June 1992

Published in: Communications in Statistics. Stochastic Models (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/15326349108807195

zbMATH Keywords

weight functioncapture-recapture experimentstandard errorsbeta distributionjackknife estimatorestimation of population sizemartingale estimating equationheterogeneity of capture probabilitieszero mean martingales

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Sampling theory, sample surveys (62D05) Martingales with continuous parameter (60G44)

Related Items (11)

Martingales in mark-recapture experiments with constant recruitment and survival ⋮ A Review of the Use of Conditional Likelihood in Capture-Recapture Experiments ⋮ Estimating the size of a closed population from a single mark-recapture regime by using a diffusion probability of marked individuals ⋮ Bayesian population estimation for small sample capture-recapture data using noninformative priors ⋮ Estimation of the Size of an Open Population from Capture‐Recapture Data Using Weighted Martingale Methods ⋮ Capture–Recapture When Time and Behavioral Response Affect Capture Probabilities ⋮ Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures ⋮ The Stratified Petersen Estimator with A Known Number of Unread Tags ⋮ A Unified Parametric Regression Model for Recapture Studies with Random Removals in Continuous Time ⋮ Mixture Models for Estimating the Size of a Closed Population When Capture Rates Vary among Individuals ⋮ Cormack-Jolly-Seber model with environmental covariates: A P-spline approach

This page was built for publication: A method of inference for a capture-recapture experiment in discrete time with variable capture probabilities