Variable selection for zero‐inflated and overdispersed data with application to health care demand in Germany

DOI10.1002/BIMJ.201400143zbMath1336.62031OpenAlexW1958419648WikidataQ30968776 ScholiaQ30968776MaRDI QIDQ3465343

Shuangge Ma, C. Y. Wang, Zhu Wang

Publication date: 21 January 2016

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

Full work available at URL: https://biostats.bepress.com/cobra/art110

zbMATH Keywords

variable selectionLASSOMCPSCADZINB

Mathematics Subject Classification ID

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

Related Items (3)

Stochastic variable selection strategies for zero-inflated models ⋮ A note on the adaptive Lasso for zero-inflated Poisson regression ⋮ Zero-inflated models for adjusting varying exposures: a cautionary note on the pitfalls of using offset

Uses Software

• glmnet
• mpath
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Cites Work

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• Two‐part regression models for longitudinal zero‐inflated count data
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• Random effect models for repeated measures of zero-inflated count data

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