Modeling zero‐inflated count data when exposure varies: With an application to tumor counts

DOI10.1002/bimj.201200021zbMath1284.62646OpenAlexW2172016017WikidataQ30664858 ScholiaQ30664858MaRDI QIDQ5410265

Gregori Baetschmann, Rainer Winkelmann

Publication date: 16 April 2014

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

Full work available at URL: https://doi.org/10.1002/bimj.201200021

zbMATH Keywords

Poisson regressioncomplementary log-log-linkextra zeros

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Related Items

Zero-Inflated Poisson Models with Measurement Error in the Response, Approximate confidence and tolerance limits for the discrete Pareto distribution for characterizing extremes in count data, Continuous time hidden Markov model for longitudinal data, Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model, Zero-inflated models for adjusting varying exposures: a cautionary note on the pitfalls of using offset

Cites Work

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