We consider the estimation of the characteristic exponent of the input to a L\'evy-driven storage model. The input process is not directly observed, but rather the workload process is sampled on an equispaced grid. 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, the distribution of the estimation errors is asymptotically normal for a high frequency sampling scheme. A resampling scheme that uses the available information in a more efficient manner is suggested and studied via simulation experiments.

翻译：我们考虑Lévy驱动存储模型输入的特征指数的估计问题。输入过程无法直接观测，而是对工作量过程进行等间距网格采样。该估计量基于与指数分布采样时刻工作量的拉普拉斯-斯蒂尔切斯变换相关的近似矩方程。对于任意观测网格，该估计量具有逐点一致性。此外，在高频采样方案下，估计误差的分布渐近正态。我们提出了一种更高效利用可用信息的重采样方案，并通过仿真实验进行了研究。