Visualizing Count Data Regressions Using Rootograms

DOI10.1080/00031305.2016.1173590OpenAlexW3100924000WikidataQ57263770 ScholiaQ57263770MaRDI QIDQ5884458

Achim Zeileis, Christian Kleiber

Publication date: 21 March 2023

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1605.01311

zbMATH Keywords

Poisson regressiongoodness of fitfinite mixturenegative binomial regressionhurdle model

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (8)

A comparison of count data models with an application to daily cigarette consumption of young persons ⋮ The VGAM package for negative binomial regression ⋮ Unnamed Item ⋮ GAMLSS: A distributional regression approach ⋮ Adapted single-cell consensus clustering (adaSC3) ⋮ A multiple inflated negative binomial hurdle regression model: analysis of the Italians' tourism behaviour during the Great Recession ⋮ A simple and useful regression model for fitting count data ⋮ Simultaneous statistical modelling of excess zeros, over/underdispersion, and multimodality with applications in hotel industry

Uses Software

• R
• MASS (R)
• ggplot2
• flexmix
• GAMLSS
• AER
• gamair

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Visualizing categorical data in ViSta
• Statistical Analysis of Financial Data in S-Plus
• Applied Econometrics with R
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Regression Analysis of Count Data
• Generalized Additive Models for Location, Scale and Shape

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