A quantitative analysis of OS noise
Alessandro Morari, Roberto Gioiosa, et al.
IPDPS 2011
We consider the problem of estimating the order of a stationary ergodic Markov chain. Our focus is on estimators which satisfy a generalized Neyman-Pearson criterion of optimality. Specifically, the optimal estimator minimizes the probability of underestimation among all estimators with probability of overestimation not exceeding a given value. Our main result identifies the best exponent of asymptotically exponential decay of the probability of underestimation. We further construct a consistent estimator, based on Kullback-Leibler divergences, which achieves the best exponent. We also present a consistent estimator involving a recursively computable statistic based on appropriate mixture distributions; this estimator also achieves the best exponent for underestimation probability. © 1996 IEEE.
Alessandro Morari, Roberto Gioiosa, et al.
IPDPS 2011
David A. Selby
IBM J. Res. Dev
Limin Hu
IEEE/ACM Transactions on Networking
Yvonne Anne Pignolet, Stefan Schmid, et al.
Discrete Mathematics and Theoretical Computer Science