Decision tree approaches for zero-inflated count data

DOI10.1080/02664760600743613zbMath1119.62303OpenAlexW2079552666MaRDI QIDQ3592628

Seohoon Jin, Seong Keon Lee

Publication date: 13 September 2007

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664760600743613

zbMATH Keywords

homogeneitymaximum likelihooddata miningdecision treezero-inflated Poisson (ZIP)

Mathematics Subject Classification ID

Applications of statistics (62P99)

Related Items (2)

Count regression trees ⋮ Regression trees and forests for non-homogeneous Poisson processes

Uses Software

• C4.5

Cites Work

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• Research and development, competition and innovation. Pseudo-maximum likelihood and simulated maximum likelihood methods applied to count data models with heterogeneity
• Multivariate Regression Trees for Analysis of Abundance Data
• Classification Trees for Multiple Binary Responses
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Score Test for Zero Inflated Generalized Poisson Regression Model
• Zero‐Altered and other Regression Models for Count Data with Added Zeros

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