Efficient ticket routing by resolution sequence mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis, Modeling And Simulation Of Network Traces For Video Transmission Over IP Networks
Journal of Integrated Design & Process Science
An efficient packet loss recovery methodology for video-over-IP
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Consistency of feature Markov processes
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates
The Journal of Machine Learning Research
| Hi-index | 754.84 |
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