10 citations · 29 across the 6 of their papers we have counts for
6 papers
Quantile autoregressive conditional heteroscedasticity
Qianqian Zhu, Songhua Tan, Yao Zheng +1
This paper proposes a novel conditional heteroscedastic time series model by applying the framework of quantile regression processes to the ARCH(\infty) form of the GARCH model. Th…
A New Measure of Model Redundancy for Compressed Convolutional Neural Networks
Feiqing Huang, Yuefeng Si, Yao Zheng +1
While recently many designs have been proposed to improve the model efficiency of convolutional neural networks (CNNs) on a fixed resource budget, theoretical understanding of thes…
High-Dimensional Low-Rank Tensor Autoregressive Time Series Modeling
Di Wang, Yao Zheng, Guodong Li
Modern technological advances have enabled an unprecedented amount of structured data with complex temporal dependence, urging the need for new methods to efficiently model and for…
High-dimensional vector autoregressive time series modeling via tensor decomposition
Di Wang, Yao Zheng, Heng Lian +1
The classical vector autoregressive model is a fundamental tool for multivariate time series analysis. However, it involves too many parameters when the number of time series and l…
Finite Time Analysis of Vector Autoregressive Models under Linear Restrictions
Yao Zheng, Guang Cheng
This paper develops a unified finite-time theory for the ordinary least squares estimation of possibly unstable and even slightly explosive vector autoregressive models under linea…
Hybrid Quantile Regression Estimation for Time Series Models with Conditional Heteroscedasticity
Yao Zheng, Qianqian Zhu, Guodong Li +1
Estimating conditional quantiles of financial time series is essential for risk management and many other applications in finance. It is well-known that financial time series displ…