Semiparametric models for capture-recapture studies with covariates

DOI10.1016/j.csda.2003.11.010zbMath1429.62642OpenAlexW2139352435MaRDI QIDQ957051

Eugene Zwane, Peter G. M. van der Heijden

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2003.11.010

zbMATH Keywords

generalized additive modelnonparametriccapture-recapturepopulation size estimationmultinomial logit model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Sampling theory, sample surveys (62D05)

Related Items (6)

A Review of the Use of Conditional Likelihood in Capture-Recapture Experiments ⋮ Estimating the size of an open population with massive datasets based on a generalized varying-coefficient model ⋮ A robust P-spline approach to closed population capture-recapture models with time dependence and heterogeneity ⋮ Estimating population size of heterogeneous populations with large data sets and a large number of parameters ⋮ A SEMIPARAMETRIC MODEL FOR A FUNCTIONAL BEHAVIOURAL RESPONSE TO CAPTURE IN CAPTURE-RECAPTURE EXPERIMENTS ⋮ Ensemble classification based on generalized additive models

Uses Software

• rms

Cites Work

• Fitting semiparametric cure models
• Semiparametric and nonparametric modeling for effect modification in matched studies
• Implementing the parametric bootstrap in capture--recapture models with continuous co\-vari\-ates.
• Logistic Regression in Capture-Recapture Models
• On the statistical analysis of capture experiments
• A nonparametric approach to the analysis of two-stage mark-recapture experiments
• Mark-Recapture Models for Line Transect Surveys
• Horvitz-Thompson Estimators for Double-Platform Line Transect Surveys
• The use of auxiliary variables in capture-recapture modelling: An overview
• Discussion comments on: The use of auxiliary variables in capture-recapture modelling. An overview
• Regression modeling strategies. With applications to linear models, logistic regression and survival analysis
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