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"M"-estimation of Boolean models for particle flow experiments

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  • Jason A. Osborne
  • Tony E. Grift

Abstract

Probability models are proposed for passage time data collected in experiments with a device that was designed to measure particle flow during aerial application of fertilizer. Maximum likelihood estimation of flow intensity is reviewed for the simple linear Boolean model, which arises with the assumption that each particle requires the same known passage time. "M"-estimation is developed for a generalization of the model in which passage times behave as a random sample from a distribution with a known mean. The generalized model improves the fit in these experiments. An estimator of total particle flow is constructed by conditioning on lengths of multiparticle clumps. Copyright (c) 2009 Royal Statistical Society.

Suggested Citation

  • Jason A. Osborne & Tony E. Grift, 2009. ""M"-estimation of Boolean models for particle flow experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 197-210.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:2:p:197-210
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    References listed on IDEAS

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    1. Catherine M. Crespi & William G. Cumberland & Sally Blower, 2005. "A Queueing Model for Chronic Recurrent Conditions under Panel Observation," Biometrics, The International Biometric Society, vol. 61(1), pages 193-198, March.
    2. Handley, John C., 2004. "Computationally efficient approximate likelihood procedures for the Boolean model," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 125-136, March.
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