One-inflation and unobserved heterogeneity in population size estimation

DOI10.1002/bimj.201600063zbMath1357.62042OpenAlexW2527201041WikidataQ39361861 ScholiaQ39361861MaRDI QIDQ2956824

Ryan T. Godwin

Publication date: 19 January 2017

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201600063

zbMATH Keywords

unobserved heterogeneitycapture-recapturenegative binomialMonte Carlo evidenceHorvitz-Thompsoncount inflationone-inflated zero-truncated negative binomial (OIZTNB) modelpositive Poisson (PP) model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Sampling theory, sample surveys (62D05)

Related Items (5)

Semiparametric empirical likelihood inference for abundance from one‐inflated capture–recapture data ⋮ Bayesian analysis of one‐inflated models for elusive population size estimation ⋮ General flation models for count data ⋮ Population size estimation based upon zero-truncated, one-inflated and sparse count data. Estimating the number of dice snakes in Graz and flare stars in the Pleiades ⋮ How many refugees and migrants died trying to reach Europe? Joint population size and total estimation

Uses Software

• Maxlik

Cites Work

• Unnamed Item
• Power series mixtures and the ratio plot with applications to zero-truncated count distribution modelling
• Heterogeneity and behavioral response in continuous time capture-recapture, with application to street cannabis use in Italy
• maxLik: a package for maximum likelihood estimation in R
• Point and interval estimation of the population size using the truncated Poisson regression model
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• A Generalization of Sampling Without Replacement From a Finite Universe
• The Use of Maximum Likelihood Estimates in $\chi^2$ Tests for Goodness of Fit

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