A uniform Berry--Esseen theorem on MM-estimators for geometrically ergodic Markov chains

preprint2012arXivOpen access

Abstract

Let {Xn}n≥0\{X_n\}_{n\ge0} be a VV-geometrically ergodic Markov chain. Given some real-valued functional FF, define Mn(α):=n−1∑k=1nF(α,Xk−1,Xk)M_n(α):=n^{-1}\sum_{k=1}^nF(α,X_{k-1},X_k), α∈A⊂Rα\in\mathcal{A}\subset \mathbb {R}. Consider an MM estimator α^n\hatα_n, that is, a measurable function of the observations satisfying Mn(α^n)≤min⁡α∈AMn(α)+cnM_n(\hatα_n)\leq \min_{α\in\mathcal{A}}M_n(α)+c_n with {cn}n≥1\{c_n\}_{n\geq1} some sequence of real numbers going to zero. Under some standard regularity and moment assumptions, close to those of the i.i.d. case, the estimator α^n\hatα_n satisfies a Berry--Esseen theorem uniformly with respect to the underlying probability distribution of the Markov chain.

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