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Statistical inference for doubly stochastic multichannel Poisson processes: A PCA approach

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  • Fernández-Alcalá, R.M.
  • Navarro-Moreno, J.
  • Ruiz-Molina, J.C.

Abstract

Efficient computational algorithms for making inferences about the intensity process of an observed doubly stochastic multichannel Poisson process are designed. The proposed solution is based on a numerical version of principal component analysis (PCA) of stochastic processes and hence it can be applied simply with knowledge of the first- and second-order moments of the intensity process of interest. The technique provided is valid for solving all types of estimation problems: filtering, prediction and smoothing.

Suggested Citation

  • Fernández-Alcalá, R.M. & Navarro-Moreno, J. & Ruiz-Molina, J.C., 2009. "Statistical inference for doubly stochastic multichannel Poisson processes: A PCA approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4322-4331, October.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4322-4331
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    References listed on IDEAS

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