
On some Limit Theorem for Markov Chain
The goal of this paper is to describe conditions which guarantee a centr...
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Moderate deviations for empirical measures for nonhomogeneous Markov chains
We prove that moderate deviations for empirical measures for countable n...
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Moderate deviation principles for kernel estimator of invariant density in bifurcating Markov chains models
Bitseki and Delmas (2021) have studied recently the central limit theore...
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Moderate deviation theorem for the NeymanPearson statistic in testing uniformity
We show that for local alternatives to uniformity which are determined b...
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Variable Length Markov Chain with Exogenous Covariates
Markov Chains with variable length are useful stochastic models for data...
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Markov Chain Models of Refugee Migration Data
The application of Markov chains to modelling refugee crises is explored...
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Lowrank MDP Approximation via Moment Coupling
We propose a novel method—based on local moment matching—to approximate ...
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Central Limit Theorem and Moderate deviation for nonhomogenenous Markov chains
Our purpose is to prove central limit theorem for countable nonhomogeneous Markov chain under the condition of uniform convergence of transition probability matrices for countable nonhomogeneous Markov chain in Cesàro sense. Furthermore, we obtain a corresponding moderate deviation theorem for countable nonhomogeneous Markov chain by GärtnerEllis theorem and exponential equivalent method.
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