no code implementations • 22 Feb 2022 • Shikai Luo, Ying Yang, Chengchun Shi, Fang Yao, Jieping Ye, Hongtu Zhu
The aim of this paper is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments.
no code implementations • 10 Jan 2022 • Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang
There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose unbounded variance.
no code implementations • 2 Nov 2020 • Xiaoyu Hu, Fang Yao
Functional principal component analysis (FPCA) is a fundamental tool and has attracted increasing attention in recent decades, while existing methods are restricted to data with a single or finite number of random functions (much smaller than the sample size $n$).
Methodology