1 citations · 1 across the 6 of their papers we have counts for
7 papers
From Vector Autoregressions to AI-based Time Series Forecasting: A Review
Likai Chen, Weining Wang
Forecasting is a central goal of time-series analysis. This review centers on three major developments in recent AI-based time-series forecasting: transformers, large pretrained mo…
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…
Online simultaneous inference for quantiles via smoothed stochastic gradient descent
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 with a bandwidth tied to…
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…
High-dimensional inference on jumps in nonparametric time series regression models
Likai Chen, Georg Keilbar, Liangjun Su +1
We study simultaneous inference on jumps in the conditional mean functions of a high-dimensional collection of heterogeneous nonparametric time series, where the number of series m…
Simultaneous Inference of a Partially Linear Model in Time Series
Jiaqi Li, Likai Chen, Kun Ho Kim +1
We introduce a new methodology to conduct simultaneous inference of the nonparametric component in partially linear time series regression models where the nonparametric part is a…
Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM
Jiaqi Li, Likai Chen, Weining Wang +1
We propose an inference method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. Our method aggregates moving su…