An improved algorithm for high-dimensional continuous threshold expectile model with variance heterogeneity
From MaRDI portal
Publication:5083335
DOI10.1080/00949655.2021.2002861OpenAlexW4200496032MaRDI QIDQ5083335
Publication date: 22 June 2022
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2021.2002861
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- Asymmetric Least Squares Estimation and Testing
- Exact post-selection inference, with application to the Lasso
- High-dimensional generalizations of asymmetric least squares regression and their applications
- High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
- One-step sparse estimates in nonconcave penalized likelihood models
- Expectile regression for analyzing heteroscedasticity in high dimension
- A continuous threshold expectile model
- Debiasing the Lasso: optimal sample size for Gaussian designs
- Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity
- A note on estimating the bent line quantile regression model
- An NLMS algorithm with TAP-selection matrix for sparse system identification
- Penalized expectile regression: an alternative to penalized quantile regression
- Calibrating nonconvex penalized regression in ultra-high dimension
- On the ``degrees of freedom of the lasso
- Regression Quantiles
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Expectile and quantile regression—David and Goliath?
- Regularization Parameter Selections via Generalized Information Criterion
- Smoothly Clipped Absolute Deviation on High Dimensions
- Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
- The Lasso for High Dimensional Regression with a Possible Change Point
This page was built for publication: An improved algorithm for high-dimensional continuous threshold expectile model with variance heterogeneity