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

Inan G.

BIOMETRICAL JOURNAL, vol.60, no.4, pp.859-864, 2018 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Letter
  • Volume: 60 Issue: 4
  • Publication Date: 2018
  • Doi Number: 10.1002/bimj.201700261
  • Journal Name: BIOMETRICAL JOURNAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.859-864
  • Keywords: count data, mean parameterization, one-inflation, zero-truncation, MODEL
  • Middle East Technical University Affiliated: Yes

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.