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Modelling the effects of partially observed covariates on Poisson process intensity

Author

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  • Stephen L. Rathbun
  • Saul Shiffman
  • Chad J. Gwaltney

Abstract

We propose an estimating function for parameters in a model for Poisson process intensity when time- or space-varying covariates are observed for both the events of the process and at sample times or locations selected from a probability-based sampling design. We investigate the large-sample properties of the proposed estimator under increasing domain asymptotics, demonstrating that it is consistent and asymptotically normally distributed. We illustrate our approach using data from an ecological momentary assessment of smoking. Copyright 2007, Oxford University Press.

Suggested Citation

  • Stephen L. Rathbun & Saul Shiffman & Chad J. Gwaltney, 2007. "Modelling the effects of partially observed covariates on Poisson process intensity," Biometrika, Biometrika Trust, vol. 94(1), pages 153-165.
    • Handle: RePEc:oup:biomet:v:94:y:2007:i:1:p:153-165
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    File URL: http://hdl.handle.net/10.1093/biomet/asm009
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    Cited by:

    1. Athanasios Christou Micheas, 2014. "Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(12), pages 2596-2615, December.
    2. Athanasios C. Micheas & Jiaxun Chen, 2018. "sppmix: Poisson point process modeling using normal mixture models," Computational Statistics, Springer, vol. 33(4), pages 1767-1798, December.

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