Queue input estimation from discrete workload observations

TL;DR

This paper addresses the challenge of estimating queue input parameters efficiently from periodic workload data, aiming to develop estimators with minimal asymptotic variance for improved statistical inference.

Contribution

It investigates the construction of statistically efficient estimators for queue input parameters based on discrete workload observations, an open problem in the field.

Findings

Identifies the key challenges in estimator efficiency for queue input inference.

Proposes approaches to minimize asymptotic variance of estimators.

Highlights open problems and future directions in statistical inference for queues.

Abstract

This note considers the problem of statistical inference of the parameters of the input process to a queue from periodic workload observations. The main focus is the open problem of constructing statistically efficient estimators for a given observation scheme, in the sense of minimizing the asymptotic variance of the estimation error.