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

J.S.K. CHAN, W.Y. WAN, Leung Ho Philip YU

Research output: Contribution to journalArticles

3 Citations (Scopus)

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. Copyright © 2014 Taylor & Francis.
Original languageEnglish
Pages (from-to)1486-1503
JournalJournal of Applied Statistics
Volume41
Issue number7
Early online dateFeb 2014
DOIs
Publication statusPublished - 2014

Citation

Chan, J. S. K., Wan, W. Y., & Yu, P. L. H. (2014). A Poisson geometric process approach for predicting drop-out and committed first-time blood donors. Journal of Applied Statistics, 41(7), 1486-1503. doi: 10.1080/02664763.2014.881781

Keywords

  • Geometric process model
  • Poisson count data
  • Trend movement
  • Mixture model
  • Bayesian method

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