Topics in matrix analysis
Bias correction in effective bandwidth estimation
Performance Evaluation
Queueing Systems: Theory and Applications
Tail Asymptotics for Discrete Event Systems
Discrete Event Dynamic Systems
Control Techniques for Complex Networks
Control Techniques for Complex Networks
Towards multihop available bandwidth estimation
ACM SIGMETRICS Performance Evaluation Review
Resource dimensioning through buffer sampling
IEEE/ACM Transactions on Networking (TON)
Inverse problems in queueing theory and Internet probing
Queueing Systems: Theory and Applications
Most likely paths to error when estimating the mean of a reflected random walk
Performance Evaluation
On the estimation of buffer overflow probabilities from measurements
IEEE Transactions on Information Theory
Entropy of ATM traffic streams: a tool for estimating QoS parameters
IEEE Journal on Selected Areas in Communications
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Loynes' distribution, which characterizes the one dimensional marginal of the stationary solution to Lindley's recursion, possesses an ultimately exponential tail for a large class of increment processes. If one can observe increments but does not know their probabilistic properties, what are the statistical limits of estimating the tail exponent of Loynes' distribution? We conjecture that in broad generality a consistent sequence of non-parametric estimators can be constructed that satisfies a large deviation principle. We present rigorous support for this conjecture under restrictive assumptions and simulation evidence indicating why we believe it to be true in greater generality.