Prediction of Components in Random Sums

Muneya Matsui ()
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Muneya Matsui: Nanzan University

Methodology and Computing in Applied Probability, 2017, vol. 19, issue 2, 573-587

Abstract: Abstract We consider predictions of the random number and the magnitude of each iid component in a random sum based on its distributional structure, where only a total value of the sum is available and where iid random components are non-negative. The problem is motivated by prediction problems in a Poisson shot noise process. In the context, although conditional moments are best possible predictors under the mean square error, only a few special cases have been investigated because of numerical difficulties. We replace the prediction problem of the process with that of a random sum, which is more general, and establish effective numerical procedures. The methods are based on conditional technique together with the Panjer recursion and the Fourier transform. In view of numerical experiments, procedures work reasonably. An application in the compound mixed Poisson process is also suggested.

Keywords: Random sum; Prediction; Conditional moments; Panjer recursion; Fourier transform; Lévy processes; 60G50; 60G25; 60-08 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11009-016-9497-4

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