no code implementations • 17 Jan 2024 • Wanrong Zhu, Zhipeng Lou, Ziyang Wei, Wei Biao Wu
We provide a rigorous theoretical guarantee for the confidence interval, demonstrating that the coverage is approximately exact with an explicit convergence rate and allowing for high confidence level inference.
no code implementations • 13 Jul 2023 • Ziyang Wei, Wanrong Zhu, Wei Biao Wu
Stochastic Gradient Descent (SGD) is one of the simplest and most popular algorithms in modern statistical and machine learning due to its computational and memory efficiency.
no code implementations • 1 Jun 2022 • Jiti Gao, Bin Peng, Wei Biao Wu, Yayi Yan
In this paper, we consider a wide class of time-varying multivariate causal processes which nests many classic and new examples as special cases.
no code implementations • 15 Dec 2020 • Sayar Karmakar, Marek Chudy, Wei Biao Wu
After validating our approach using simulations we also propose a novel bootstrap based method that can boost the coverage of the theoretical intervals.
Prediction Intervals Time Series Analysis Methodology Econometrics Statistics Theory Statistics Theory
no code implementations • 15 Jun 2020 • Ayman Moawad, Ehsan Islam, Namdoo Kim, Ram Vijayagopal, Aymeric Rousseau, Wei Biao Wu
The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems, while allowing for underlying complex subsystem level interactions.
no code implementations • 10 Feb 2020 • Wanrong Zhu, Xi Chen, Wei Biao Wu
This approach fits in an online setting and takes full advantage of SGD: efficiency in computation and memory.