Input estimation from discrete workload observations in a L\'evy-driven storage system
Probability
2024-08-29 v3 Statistics Theory
Statistics Theory
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.
Cite
@article{arxiv.2205.09980,
title = {Input estimation from discrete workload observations in a L\'evy-driven storage system},
author = {Dennis Nieman and Michel Mandjes and Liron Ravner},
journal= {arXiv preprint arXiv:2205.09980},
year = {2024}
}