Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference

• URL: http://arxiv.org/abs/2312.10894v1
• Date: Mon, 18 Dec 2023 02:51:57 GMT
• Title: Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference
• Authors: Dongyan Huo, Yudong Chen, Qiaomin Xie
• Abstract summary: We study the effectiveness of using a constant stepsize in statistical inference via linear approximation (LSA) algorithms with Markovian data. Our results show that using a constant stepsize enjoys easy hyper parameter tuning, fast convergence, and consistently better CI coverage, especially when data is limited.
• Score: 9.689344942945652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the effectiveness of using a constant stepsize in statistical inference via linear stochastic approximation (LSA) algorithms with Markovian data. After establishing a Central Limit Theorem (CLT), we outline an inference procedure that uses averaged LSA iterates to construct confidence intervals (CIs). Our procedure leverages the fast mixing property of constant-stepsize LSA for better covariance estimation and employs Richardson-Romberg (RR) extrapolation to reduce the bias induced by constant stepsize and Markovian data. We develop theoretical results for guiding stepsize selection in RR extrapolation, and identify several important settings where the bias provably vanishes even without extrapolation. We conduct extensive numerical experiments and compare against classical inference approaches. Our results show that using a constant stepsize enjoys easy hyperparameter tuning, fast convergence, and consistently better CI coverage, especially when data is limited.

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