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A Poisson geometric process approach for predicting drop-out and committed first-time blood donors

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  • J.S.K. Chan
  • W.Y. Wan
  • P.L.H. Yu

Abstract

A Poisson geometric process (PGP) model is proposed to study individual blood donation patterns for a blood donor retention program. Extended from the geometric process (GP) model of Lam [16], the PGP model captures the rather pronounced trend patterns across clusters of donors via the ratio parameters in a mixture setting. Within the state-space modeling framework, it allows for overdispersion by equating the mean of the Poisson data distribution to a latent GP. Alternatively, by simply setting, the mean of the Poisson distribution to be the mean of a GP, it has equidispersion. With the group-specific mean and ratio functions, the mixture PGP model facilitates classification of donors into committed, drop-out and one-time groups. Based on only two years of observations, the PGP model nicely predicts donors' future donations to foster timely recruitment decision. The model is implemented using a Bayesian approach via the user-friendly software WinBUGS.

Suggested Citation

  • J.S.K. Chan & W.Y. Wan & P.L.H. Yu, 2014. "A Poisson geometric process approach for predicting drop-out and committed first-time blood donors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1486-1503, July.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1486-1503
    DOI: 10.1080/02664763.2014.881781
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    1. J. Durbin & S. J. Koopman, 2000. "Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
    2. Wan, Wai-Yin & Chan, Jennifer So-Kuen, 2011. "Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 687-702, January.
    3. Lam Yeh & So Kuen Chan, 1998. "Statistical inference for geometric processes with lognormal distribution," Computational Statistics & Data Analysis, Elsevier, vol. 27(1), pages 99-112, March.
    4. Richard A. Davis, 2003. "Observation-driven models for Poisson counts," Biometrika, Biometrika Trust, vol. 90(4), pages 777-790, December.
    5. Chan, Jennifer S. K. & Lam, Yeh & Leung, Doris Y. P., 2004. "Statistical inference for geometric processes with gamma distributions," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 565-581, October.
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    Cited by:

    1. Arnold, Richard & Chukova, Stefanka & Hayakawa, Yu & Marshall, Sarah, 2020. "Geometric-Like Processes: An Overview and Some Reliability Applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).

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