Automatic Optimal Batch Size Selection for Recursive Estimators of Time-Average Covariance Matrix

Kin Wai Chan and Chun Yip Yau

Journal of the American Statistical Association, 2017, vol. 112, issue 519, 1076-1089

Abstract: The time-average covariance matrix (TACM) Σ:=∑k∈ZΓk$\bm {\Sigma }:=\sum _{k\in \mathbb {Z}}\bm {\Gamma }_k$, where Γk is the auto-covariance function, is an important quantity for the inference of the mean of an Rd$\mathbb {R}^d$-valued stationary process (d ⩾ 1). This article proposes two recursive estimators for Σ with optimal asymptotic mean square error (AMSE) under different strengths of serial dependence. The optimal estimator involves a batch size selection, which requires knowledge of a smoothness parameter ϒβ:=∑k∈Z|k|βΓk$\bm {\Upsilon }_{\beta }:=\sum _{k\in \mathbb {Z}} |k|^{\beta } \bm {\Gamma }_k$, for some β. This article also develops recursive estimators for ϒβ. Combining these two estimators, we obtain a fully automatic procedure for optimal online estimation for Σ. Consistency and convergence rates of the proposed estimators are derived. Applications to confidence region construction and Markov chain Monte Carlo convergence diagnosis are discussed. Supplementary materials for this article are available online.

Date: 2017
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DOI: 10.1080/01621459.2016.1189337

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