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Matrix asymptotic calculus for plug-in maximum likelihood estimators in finite Markov chains
In this work, we develop a unified matrix-level asymptotic calculus for plug-in non-parametric maximum likelihood estimators in finite Markov models. Starting from the asymptotic distribution of the estimated transition matrix, the limiting object is kept in its natural matrix form as a Gaussian ran
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