Zero-inflated modeling part II: Zero-inflated models for complex data structures

Derek S. Young, Eric S. Roemmele, Xuan Shi

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

The prequel to this review provided an extensive treatment of classic zero-inflated count regression models where a univariate discrete distribution is used for the count regression component of the model. The treatment of zero inflation beyond the classic univariate count regression setting has seen a substantial increase in recent years. This second review paper surveys some of this recent literature and focuses on important developments in handling zero inflation for correlated count settings, discrete time series models, spatial models, and multivariate models. We discuss some of the available computational tools for performing estimation in these settings, while again highlighting the diverse data problems that have been addressed using these methods. This article is categorized under: Statistical Models > Multivariate Models Statistical Models > Generalized Linear Models Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory.

Original languageEnglish
Article numbere1540
JournalWiley Interdisciplinary Reviews: Computational Statistics
Volume14
Issue number2
DOIs
StatePublished - Mar 1 2022

Bibliographical note

Publisher Copyright:
© 2020 Wiley Periodicals LLC.

Keywords

  • Bayesian hierarchical models
  • count time series
  • diagonal inflation
  • generalized linear mixed models
  • multivariate counts
  • spatial modeling

ASJC Scopus subject areas

  • Statistics and Probability

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