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

Authors:
R. M. Fernández-Alcalá;J. Navarro-Moreno;J. C. Ruiz-Molina
Affiliations:
University of Jaén, Department of Statistics and Operations Research, Campus Las Lagunillas, 23071 Jaén, Spain;University of Jaén, Department of Statistics and Operations Research, Campus Las Lagunillas, 23071 Jaén, Spain;University of Jaén, Department of Statistics and Operations Research, Campus Las Lagunillas, 23071 Jaén, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2009

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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.