Quasi-negative binomial distribution: Properties and applications

Shubiao Li, Fang Yang, Felix Famoye, Carl Lee, Dennis Black

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In this paper, a quasi-negative binomial distribution (QNBD) derived from the class of generalized Lagrangian probability distributions is studied. The negative binomial distribution is a special case of QNBD. Some properties of QNBD, including the upper tail behavior and limiting distributions, are investigated. It is shown that the moments do not exist in some situations and the limiting distribution of QNBD is the generalized Poisson distribution under certain conditions. A zero-inflated QNBD is also defined. Applications of QNBD and zero-inflated QNBD in various fields are presented and compared with some other existing distributions including Poisson, generalized Poisson and negative binomial distributions as well as their zero-inflated versions. In general, the QNBD or its zero-inflated version performs better than the other models based on the chi-square statistic and the Akaike Information Criterion, especially for the cases where the data are highly skewed, have heavy tails or excessive numbers of zeros.

Original languageEnglish
Pages (from-to)2363-2371
Number of pages9
JournalComputational Statistics and Data Analysis
Volume55
Issue number7
DOIs
StatePublished - Jul 1 2011

Keywords

  • Lagrange expansion
  • Limiting distribution
  • Tail property
  • Zero-inflation

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