On Nonparametric Estimation in Online Problems
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Offline estimators are often inadequate for real-time applications. Nevertheless, many online estimators are found by sacrificing some statistical efficiency. This paper presents a general framework to understand and construct efficient nonparametric estimators for online problems. Statistically, we choose long-run variance as an exemplary estimand and derive the first set of sufficient conditions for O(1)-time or O(1)-space update, which allows methodological generation of estimators. Our asymptotic theory shows that the generated estimators dominate existing alternatives. Computationally, we introduce mini-batch estimation to accelerate online estimators for real-time applications. Implementation issues such as automatic optimal parameters selection are discussed. Practically, we demonstrate how to use our framework with recent development in change point detection, causal inference, and stochastic approximation. We also illustrate the strength of our estimators in some classical problems such as Markov chain Monte Carlo convergence diagnosis and confidence interval construction.
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