Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Jia, Bochao"'
Publikováno v:
In Heliyon 15 August 2024 10(15)
Uncovering the heterogeneity in the disease progression of Alzheimer's is a key factor to disease understanding and treatment development, so that interventions can be tailored to target the subgroups that will benefit most from the treatment, which
Externí odkaz:
http://arxiv.org/abs/2103.06363
Publikováno v:
Journal of the American Statistical Association, 2020
This paper proposes an innovative method for constructing confidence intervals and assessing p-values in statistical inference for high-dimensional linear models. The proposed method has successfully broken the high-dimensional inference problem into
Externí odkaz:
http://arxiv.org/abs/2010.08864
Akademický článek
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Autor:
Qi, Yingchun, Sha, Pengwei, Yang, Kun, Jia, Bochao, Xu, Zezhou, Yang, Yanan, Guo, Yunting, Li, Lunxiang, Cao, Qing, Zou, Tingting, Yang, Jianjun, Yu, Zhenglei, Mu, Zhengzhi
Publikováno v:
In Journal of Materials Research and Technology May-June 2023 24:9462-9475
Autor:
Xu, Zezhou, Guo, Yunting, Liu, Yuting, Jia, Bochao, Sha, Pengwei, Li, Lunxiang, Yu, Zhenglei, Zhang, Zhihui, Ren, Luquan
Publikováno v:
In Applied Surface Science 1 March 2023 612
Autor:
Jia, Bochao, Liang, Faming
The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. Most existing methods for Gaussian graphical models assume that the data are homogeneous, i.e., all samples are drawn
Externí odkaz:
http://arxiv.org/abs/1805.02547
Motivated by the need to study the molecular mechanism underlying Type 1 Diabetes (T1D) with the gene expression data collected from both the patients and healthy controls at multiple time points, we propose an innovative method for jointly estimatin
Externí odkaz:
http://arxiv.org/abs/1805.02620
Autor:
Jia, Bochao
Envelope model also known as multivariate regression model was proposed to solve the multiple response regression problems. It measures the linear association between predictors and multiple responses by using the minimal reducing subspace of the cov
Externí odkaz:
http://arxiv.org/abs/1805.01864
Missing data are frequently encountered in high-dimensional problems, but they are usually difficult to deal with using standard algorithms, such as the expectation-maximization (EM) algorithm and its variants. To tackle this difficulty, some problem
Externí odkaz:
http://arxiv.org/abs/1802.02251