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arXiv · 2205.09980

Input estimation from discrete workload observations in a L\'evy-driven storage system

Dennis Nieman·Michel Mandjes·Liron Ravner
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Abstract

Our goal is to estimate the characteristic exponent of the input to a L\'evy-driven storage system from a sample of equispaced workload observations. The estimator relies on an approximate moment equation associated with the Laplace-Stieltjes transform of the workload at exponentially distributed sampling times. The estimator is pointwise consistent for any observation grid. Moreover, a high frequency sampling scheme yields asymptotically normal estimation errors for a class of input processes. A resampling scheme that uses the available information in a more efficient manner is suggested and assessed via simulation experiments.

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Dennis Nieman, Michel Mandjes, Liron Ravner. 2022-05-20. Input estimation from discrete workload observations in a L\'evy-driven storage system. https://doi.org/10.1016/j.spl.2024.110250

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