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20152026
most citedLocally sparse quantile estimation for a partially functional interaction model

3 citations · 7 across the 9 of their papers we have counts for

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10 papers · 1 filter

stat.ME2026

Local spectral clustering for heterogeneous clustering structures

Yuanxing Chen, Qingzhao Zhang, Yuhong Yang

Classical clustering methods typically assume that all informative features support a single latent partition of the observations. This assumption can be overly restrictive for mod…

stat.ME2026

Adaptive Multi-Prior Lasso for High-Dimensional Generalized Linear Models

Fuzhi Xu, Weijuan Liang, Shuangge Ma +1

Incorporation of external information into high-dimensional modeling for gene expression data has been shown, both theoretically and empirically, to substantially enhance performan…

stat.ME2023

Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions

Weijuan Liang, Qingzhao Zhang, Shuangge Ma

With the development of data collection techniques, analysis with a survival response and high-dimensional covariates has become routine. Here we consider an interaction model, whi…

stat.ME2023★ 1 cited

Regulation-incorporated Gene Expression Network-based Heterogeneity Analysis

Rong Li, Qingzhao Zhang, Shuangge Ma

Gene expression-based heterogeneity analysis has been extensively conducted. In recent studies, it has been shown that network-based analysis, which takes a system perspective and…

stat.ME2023★ 3 cited

Locally sparse quantile estimation for a partially functional interaction model

Weijuan Liang, Qingzhao Zhang, Shuangge Ma

Functional data analysis has been extensively conducted. In this study, we consider a partially functional model, under which some covariates are scalars and have linear effects, w…

stat.ME2022★ 2 cited

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis

Yuanxing Chen, Qingzhao Zhang, Shuangge Ma +1

In diverse fields ranging from finance to omics, it is increasingly common that data is distributed and with multiple individual sources (referred to as ``clients'' in some studies…