Robust integrative analysis via quantile regression with homogeneity and sparsity
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Publication:6616189
DOI10.1016/j.jspi.2024.106196MaRDI QIDQ6616189
Wei Zhong, Hao Zeng, Tuo Liu, Chuang Wan
Publication date: 8 October 2024
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Nearly unbiased variable selection under minimax concave penalty
- A note on the consistency of Schwarz's criterion in linear quantile regression with the SCAD penalty
- Globally adaptive quantile regression with ultra-high dimensional data
- Estimating the dimension of a model
- Convex analysis approach to d. c. programming: Theory, algorithms and applications
- On parameters of increasing dimensions
- Nonconcave penalized likelihood with a diverging number of parameters.
- High-dimensional integrative analysis with homogeneity and sparsity recovery
- A nonconvex model with minimax concave penalty for image restoration
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Adaptive robust variable selection
- Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
- Integrative analysis of prognosis data on multiple cancer subtypes
- Integrative analysis and variable selection with multiple high-dimensional data sets
- Extended Bayesian information criteria for model selection with large model spaces
- The Group Lasso for Logistic Regression
- Unified LASSO Estimation by Least Squares Approximation
- Regression Quantiles
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Model Selection via Bayesian Information Criterion for Quantile Regression Models
- ROBUST SUBGROUP IDENTIFICATION
- Smoothly Clipped Absolute Deviation on High Dimensions
- Model Selection and Estimation in Regression with Grouped Variables
- Tuning parameter selectors for the smoothly clipped absolute deviation method
- Tuning Parameter Selection in High Dimensional Penalized Likelihood
- Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization
- Individualized Multidirectional Variable Selection
- Poststratification fusion learning in longitudinal data analysis
- Integrative analysis of `-omics' data using penalty functions
- ADMM for High-Dimensional Sparse Penalized Quantile Regression
- Robust nonparametric integrative analysis to decipher heterogeneity and commonality across subgroups using sparse boosting
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