Input estimation from discrete workload observations in a Lévy-driven storage system

Dennis Nieman, Michel Mandjes and Liron Ravner

Statistics & Probability Letters, 2025, vol. 216, issue C

Abstract: Our goal is to estimate the characteristic exponent of the input to a Lévy-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.

Keywords: Lévy-driven storage system; Discrete workload observations; High-frequency sampling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.spl.2024.110250

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