A new approach of subgroup identification for high-dimensional longitudinal data
From MaRDI portal
Publication:5036847
DOI10.1080/00949655.2020.1764555OpenAlexW3025470947MaRDI QIDQ5036847
Publication date: 23 February 2022
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2020.1764555
longitudinal datapersonalized medicinechange point detectionsparse boostinghomogeneity pursuitminimum descriptive length
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Structure identification in panel data analysis
- Estimation of semivarying coefficient time series models with ARMA errors
- Bayesian empirical likelihood for quantile regression
- Boosting additive models using component-wise P-splines
- Recursive partitioning and applications
- A decision-theoretic generalization of on-line learning and an application to boosting
- Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients
- A novel partial-linear single-index model for time series data
- Pathwise coordinate optimization
- Identification of subpopulations with distinct treatment benefit rate using the Bayesian tree
- Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
- Personalized treatment for longitudinal data using unspecified random-effects model
- Averaged gene expressions for regression
- Model Selection and the Principle of Minimum Description Length
- Boosting With theL2Loss
- A Simultaneous Confidence Band for Dense Longitudinal Regression
- Grouping Pursuit Through a Regularization Solution Surface
- Effective dimension reduction for sparse functional data
- General Sparse Boosting: Improving Feature Selection of L2Boosting by Correlation-Based Penalty Family
- Simultaneous Grouping Pursuit and Feature Selection Over an Undirected Graph
- Homogeneity Pursuit
- Unified inference for sparse and dense longitudinal models
- Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR
This page was built for publication: A new approach of subgroup identification for high-dimensional longitudinal data