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Statistical inference for zero-and-one-inflated poisson models

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  • Yincai Tang
  • Wenchen Liu
  • Ancha Xu

Abstract

In this paper, a zero-and-one-inflated Poisson (ZOIP) model is studied. The maximum likelihood estimation and the Bayesian estimation of the model parameters are obtained based on data augmentation method. A simulation study based on proposed sampling algorithm is conducted to assess the performance of the proposed estimation for various sample sizes. Finally, two real data-sets are analysed to illustrate the practicability of the proposed method.

Suggested Citation

  • Yincai Tang & Wenchen Liu & Ancha Xu, 2017. "Statistical inference for zero-and-one-inflated poisson models," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 1(2), pages 216-226, July.
    • Handle: RePEc:taf:tstfxx:v:1:y:2017:i:2:p:216-226
    DOI: 10.1080/24754269.2017.1400419
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    Cited by:

    1. Zhou, Can & Jiao, Yan & Browder, Joan, 2019. "K-aggregated transformation of discrete distributions improves modeling count data with excess ones," Ecological Modelling, Elsevier, vol. 407(C), pages 1-1.
    2. Sunisa Junnumtuam & Sa-Aat Niwitpong & Suparat Niwitpong, 2022. "A Zero-and-One Inflated Cosine Geometric Distribution and Its Application," Mathematics, MDPI, vol. 10(21), pages 1-22, October.
    3. Wenchen Liu & Yincai Tang & Ancha Xu, 2021. "Zero-and-one-inflated Poisson regression model," Statistical Papers, Springer, vol. 62(2), pages 915-934, April.

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