4 citations · 10 across the 7 of their papers we have counts for
8 papers
Boot-AVT: Asymptotically Anytime-Valid Testing with Degenerate U-Statistics via Bootstrap
Jixuan Liu, Long Feng, Liuhua Peng +1
Many nonparametric tests are based on -statistics that are degenerate under the null hypothesis. We develop Boot-AVT, a bootstrap procedure for asymptotically anytime-valid test…
Conformalized Robust Principal Component Analysis
Liangliang Yuan, Lei Wang, Quan Kong +1
Robust principal component analysis (RPCA) is a widely used technique for recovering low-rank structure from matrices with missing entries and sparse, possibly large-magnitude corr…
Statistical Inference for Ultrahigh Dimensional Location Parameter Based on Spatial Median
Guanghui Cheng, Liuhua Peng, Changliang Zou
Motivated by the widely used geometric median-of-means estimator in machine learning, this paper studies statistical inference for ultrahigh dimensionality location parameter based…
Extreme Continuous Treatment Effects: Measures, Estimation and Inference
Wei Huang, Shuo Li, Liuhua Peng
This paper concerns estimation and inference for treatment effects in deep tails of the counterfactual distribution of unobservable potential outcomes corresponding to a continuous…
Nonparametric Feature Selection by Random Forests and Deep Neural Networks
Xiaojun Mao, Liuhua Peng, Zhonglei Wang
Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and usele…
Bootstrap inference for the finite population total under complex sampling designs
Zhonglei Wang, Jae Kwang Kim, Liuhua Peng
Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results under complex survey sampling. Most studies about bootstrap-based inference are de…
Variable Importance Assessments and Backward Variable Selection for High-Dimensional Data
Liuhua Peng, Long Qu, Dan Nettleton
Variable selection in high-dimensional scenarios is of great interested in statistics. One application involves identifying differentially expressed genes in genomic analysis. Exis…
Distributed Statistical Inference for Massive Data
Song Xi Chen, Liuhua Peng
This paper considers distributed statistical inference for general symmetric statistics %that encompasses the U-statistics and the M-estimators in the context of massive data where…