One-inflation and unobserved heterogeneity in population size estimation by Ryan T. Godwin

Gul Inan*

*Corresponding author for this work

Research output: Contribution to journalLetterpeer-review

1 Citation (Scopus)

Abstract

In this study, we would like to show that the one-inflated zero-truncated negative binomial (OIZTNB) regression model can be easily implemented in R via built-in functions when we use mean-parameterization feature of negative binomial distribution to build OIZTNB regression model. From the practitioners' point of view, we believe that this approach presents a computationally convenient way for implementation of the OIZTNB regression model.

Original languageEnglish
Pages (from-to)859-864
Number of pages6
JournalBiometrical Journal
Volume60
Issue number4
DOIs
Publication statusPublished - Jul 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Keywords

  • count data
  • mean parameterization
  • one-inflation
  • zero-truncation

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