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A class of bivariate Poisson processes

Author

Listed:
  • Griffiths, R. C.
  • Milne, R. K.

Abstract

Tyan and Thomas (J. Multivariate Anal. 5 (1975), 227-235), have given a characterization of a class of bivariate distributions which yields, as a special case, a characterization of a class of bivariate Poisson distributions. In this paper we develop an analogous characterization of a class of bivariate Poisson processes and give some properties and examples of such processes.

Suggested Citation

  • Griffiths, R. C. & Milne, R. K., 1978. "A class of bivariate Poisson processes," Journal of Multivariate Analysis, Elsevier, vol. 8(3), pages 380-395, September.
    • Handle: RePEc:eee:jmvana:v:8:y:1978:i:3:p:380-395
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    Cited by:

    1. Lijoi, Antonio & Nipoti, Bernardo & Prünster, Igor, 2014. "Dependent mixture models: Clustering and borrowing information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 417-433.
    2. Alexander Chechelnitski, 1999. "Asymptotical properties of erlang queueing systems with a dependent Poisson input flow," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 7(2), pages 221-229, December.
    3. Antonio Lijoi & Igor Prünster, 2014. "Discussion of “On simulation and properties of the stable law” by L. Devroye and L. James," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 371-377, August.
    4. Antonio Lijoi & Bernardo Nipoti, 2013. "A class of hazard rate mixtures for combining survival data from different experiments," DEM Working Papers Series 059, University of Pavia, Department of Economics and Management.
    5. Antonio Lijoi & Bernardo Nipoti, 2014. "A Class of Hazard Rate Mixtures for Combining Survival Data From Different Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 802-814, June.
    6. Antonio Lijoi & Bernardo Nipoti & Igor Prünster, 2013. "Dependent mixture models: clustering and borrowing information," DEM Working Papers Series 046, University of Pavia, Department of Economics and Management.

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