Computation and analysis of change points with different jump locations in high-dimensional regression
From MaRDI portal
Publication:6579394
DOI10.1007/s00362-023-01461-wMaRDI QIDQ6579394
Jian Huang, Yan-Yan Liu, Xinfeng Yang, Lican Kang, Yu Ling Jiao
Publication date: 25 July 2024
Published in: Statistical Papers (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Nearly unbiased variable selection under minimax concave penalty
- Wild binary segmentation for multiple change-point detection
- Statistics for high-dimensional data. Methods, theory and applications.
- Estimating networks with jumps
- Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons
- Simultaneous analysis of Lasso and Dantzig selector
- A nonconvex model with minimax concave penalty for image restoration
- Forward Regression for Ultra-High Dimensional Variable Screening
- On Grouping for Maximum Homogeneity
- Multiple Change-Points Estimation in Linear Regression Models via Sparse Group Lasso
- Grouped Patterns of Heterogeneity in Panel Data
- Sparsity and Smoothness Via the Fused Lasso
- Fast and Scalable Algorithm for Detection of Structural Breaks in Big VAR Models
- Multiple Change-Point Estimation With a Total Variation Penalty
- Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
- Strong Rules for Discarding Predictors in Lasso-Type Problems
- The Lasso for High Dimensional Regression with a Possible Change Point
- Multiple-Change-Point Detection for Auto-Regressive Conditional Heteroscedastic Processes
- Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization
This page was built for publication: Computation and analysis of change points with different jump locations in high-dimensional regression
