From the 2 of 5 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
5 papers
From Vector Autoregressions to AI-based Time Series Forecasting: A Review
Likai Chen, Weining Wang
The paper reviews recent AI-driven time‑series forecasting methods—including transformers, large pretrained zero‑shot models, and diffusion‑based forecasters—and relates them to tr…
High-dimensional inference on jumps in nonparametric time series regression models
Likai Chen, Georg Keilbar, Liangjun Su +1
The paper develops statistical tests for detecting and comparing jumps in the conditional mean of many nonparametric time series, even when the number of series exceeds the sample…
Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
Ziyang Wei, Jiaqi Li, Likai Chen +1
This paper develops asymptotic theory for quantile estimation via stochastic gradient descent (SGD) with a constant learning rate. The quantile loss function is neither smooth nor…
Estimation of High-dimensional Nonlinear Vector Autoregressive Models
Yuefeng Han, Likai Chen, Wei Biao Wu
High-dimensional vector autoregressive (VAR) models have numerous applications in fields such as econometrics, biology, climatology, among others. While prior research has mainly f…
Smoothed SGD for quantiles: Bahadur representation and Gaussian approximation
Likai Chen, Georg Keilbar, Wei Biao Wu
This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the score function in the conventional SGD…