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

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Author Info
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.

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File URL: http://www.sciencedirect.com/science/article/B6V8V-4WGK4CP-1/2/b278a98e2c89cf70a1dd1d797ca7c1e5
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Publisher Info
Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 53 (2009)
Issue (Month): 12 (October)
Pages: 4322-4331
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4322-4331

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Web page: http://www.elsevier.com/locate/csda

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This page was last updated on 2009-12-30.


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