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A note on concentration inequalities for the overlapped batch mean variance estimators for Markov chains

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arxiv 2505.08456 v1 pith:XHGAPQCM submitted 2025-05-13 math.PR math.STstat.MLstat.TH

classification math.PRmath.STstat.MLstat.TH
keywords markovchainsconcentrationvarianceasymptoticbatchchainestimators
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abstract

In this paper, we study the concentration properties of quadratic forms associated with Markov chains using the martingale decomposition method introduced by Atchad\'e and Cattaneo (2014). In particular, we derive concentration inequalities for the overlapped batch mean (OBM) estimators of the asymptotic variance for uniformly geometrically ergodic Markov chains. Our main result provides an explicit control of the $p$-th moment of the difference between the OBM estimator and the asymptotic variance of the Markov chain with explicit dependence upon $p$ and mixing time of the underlying Markov chain.

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  1. Statistical inference for Linear Stochastic Approximation with Markovian Noise

    stat.ML 2025-05 conditional novelty 7.0 of 10

    Polyak-Ruppert averaged linear stochastic approximation with Markovian noise achieves Berry-Esseen rate O(n^{-1/4}) in Kolmogorov distance, and a multiplier subsample bootstrap achieves coverage error O(n^{-1/10}).

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