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Heterogeneous data modeling with two-component Weibull--Poisson distribution

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  • �lk� Erisoglu
  • Murat Erisoglu
  • Nazif Çalis

Abstract

The mixture distribution models are more useful than pure distributions in modeling of heterogeneous data sets. The aim of this paper is to propose mixture of Weibull--Poisson (WP) distributions to model heterogeneous data sets for the first time. So, a powerful alternative mixture distribution is created for modeling of the heterogeneous data sets. In the study, many features of the proposed mixture of WP distributions are examined. Also, the expectation maximization (EM) algorithm is used to determine the maximum-likelihood estimates of the parameters, and the simulation study is conducted for evaluating the performance of the proposed EM scheme. Applications for two real heterogeneous data sets are given to show the flexibility and potentiality of the new mixture distribution.

Suggested Citation

  • �lk� Erisoglu & Murat Erisoglu & Nazif Çalis, 2013. "Heterogeneous data modeling with two-component Weibull--Poisson distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(11), pages 2451-2461, November.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2451-2461
    DOI: 10.1080/02664763.2013.818108
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