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pro vyhledávání: '"Peng, Liuhua"'
Motivated by the widely used geometric median-of-means estimator in machine learning, this paper studies statistical inference for ultrahigh dimensionality location parameter based on the sample spatial median under a general multivariate model, incl
Externí odkaz:
http://arxiv.org/abs/2301.03126
This paper concerns estimation and inference for treatment effects in deep tails of the counterfactual distribution of unobservable potential outcomes corresponding to a continuously valued treatment. We consider two measures for the deep tail charac
Externí odkaz:
http://arxiv.org/abs/2209.00246
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 useless features. Herein, we propose a nonparametric feature selection algo
Externí odkaz:
http://arxiv.org/abs/2201.06821
Autor:
Cofré Lizama, L. Eduardo, Panisset, Maya G., Peng, Liuhua, Tan, Ying, Kalincik, Tomas, Galea, Mary P.
Publikováno v:
In Gait & Posture June 2024 111:14-21
Publikováno v:
In Journal of Statistical Planning and Inference September 2023 226:86-99
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 developed under simple random sampling and stratified random sampling. I
Externí odkaz:
http://arxiv.org/abs/1901.01645
Akademický článek
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Variable selection in high-dimensional scenarios is of great interested in statistics. One application involves identifying differentially expressed genes in genomic analysis. Existing methods for addressing this problem have some limits or disadvant
Externí odkaz:
http://arxiv.org/abs/1806.06468
Autor:
Chen, Song Xi, Peng, Liuhua
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 the data can be stored at multiple platforms in different locations.
Externí odkaz:
http://arxiv.org/abs/1805.11214
Publikováno v:
In Journal of Multivariate Analysis November 2021 186