Regression‐based heterogeneity analysis to identify overlapping subgroup structure in high‐dimensional data
From MaRDI portal
Publication:6068650
DOI10.1002/bimj.202100119zbMath1523.62161arXiv2211.15152MaRDI QIDQ6068650
Yifan Sun, Unnamed Author, Xinyan Fan, Unnamed Author
Publication date: 15 December 2023
Published in: Biometrical Journal (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2211.15152
Cites Work
- Unnamed Item
- High-dimensional regression with Gaussian mixtures and partially-latent response variables
- Regularized fuzzy clusterwise ridge regression
- \(\ell_{1}\)-penalization for mixture regression models
- Finite mixture regression: a sparse variable selection by model selection for clustering
- A globally convergent algorithm for Lasso-penalized mixture of linear regression models
- Inverse regression approach to robust nonlinear high-to-low dimensional mapping
- Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models
- Structured analysis of the high-dimensional FMR model
- Quantile-regression-based clustering for panel data
- Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model
- Model-based regression clustering for high-dimensional data: application to functional data
- Regularization in Finite Mixture of Regression Models with Diverging Number of Parameters
- Variable Selection in Finite Mixture of Regression Models
- Grouped Patterns of Heterogeneity in Panel Data
- On approximations via convolution-defined mixture models
- Approximation by finite mixtures of continuous density functions that vanish at infinity
- Semisoft clustering of single-cell data
- Variable neighborhood search: Principles and applications
- Histopathological imaging‐based cancer heterogeneity analysis via penalized fusion with model averaging
This page was built for publication: Regression‐based heterogeneity analysis to identify overlapping subgroup structure in high‐dimensional data