Inferring serial correlation with dynamic backgrounds

Song Wei · Yao Xie · Dobromir Rahnev

Keywords: [ Algorithms ] [ Distributed Inference ] [ Algorithms -> Regression; Optimization -> Convex Optimization; Theory -> Learning Theory; Theory ] [ Regularization ] [ Time Series and Sequences ]

[ Abstract ]
[ Paper ]
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Wed 21 Jul 9 a.m. PDT — 11 a.m. PDT
Oral presentation: Time Series 1
Wed 21 Jul 7 a.m. PDT — 8 a.m. PDT


Sequential data with serial correlation and an unknown, unstructured, and dynamic background is ubiquitous in neuroscience, psychology, and econometrics. Inferring serial correlation for such data is a fundamental challenge in statistics. We propose a Total Variation (TV) constrained least square estimator coupled with hypothesis tests to infer the serial correlation in the presence of unknown and unstructured dynamic background. The TV constraint on the dynamic background encourages a piecewise constant structure, which can approximate a wide range of dynamic backgrounds. The tuning parameter is selected via the Ljung-Box test to control the bias-variance trade-off. We establish a non-asymptotic upper bound for the estimation error through variational inequalities. We also derive a lower error bound via Fano's method and show the proposed method is near-optimal. Numerical simulation and a real study in psychology demonstrate the excellent performance of our proposed method compared with the state-of-the-art.

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