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For finite-alphabet stationary ergodic processes with infinite memory, Markov order estimators that optimize an information criterion over the candidate orders based on a sample of size n are investigated. Three familiar information criteria are considered: the Bayesian information criterion (BIC) with generalized penalty term yielding the penalized maximum likelihood (PML), and the normalized maximum...