Lujia Bai

I obtain my Ph.d. from Department of Statistics and Data Science at Tsinghua University, Beijing, China, advised by Prof. Weichi Wu . In 2020, I obtained my B.Sc. in the School of Statistics and Management, Shanghai University of Finance and Economics.

I am broadly interested in data with complex structure and non-stationary dynamics. My current research focuses on non-stationary time series, time-varying network, functional time series and long-range dependence. I speak Chinese, English and German as well as a little Spanish.

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We consider the problem of testing for long-range dependence in time-varying coefficient regression models, where the covariates and errors are locally stationary, allowing complex temporal dynamics and heteroscedasticity. We develop KPSS, R/S, V/S, and K/S-type statistics based on the nonparametric residuals.

This paper proposes a flexible framework for inferring large-scale time-varying and time-lagged correlation networks from multivariate or high-dimensional non-stationary time series with piecewise smooth trends.

We propose a novel difference-based and debiased long-run covariance matrix estimator for functional linear models with time-varying regression coefficients, allowing time series non-stationarity, long-range dependence, state-heteroscedasticity and their mixtures. We apply the new estimator to existing tests, overcoming the notorious non-monotonic power phenomena and improving the performance via the residual free formula.

UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.

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