Expectile regression for analyzing heteroscedasticity in high dimension
From MaRDI portal
Publication:1640971
DOI10.1016/j.spl.2018.02.006zbMath1414.62324OpenAlexW2790935913WikidataQ130198780 ScholiaQ130198780MaRDI QIDQ1640971
Yingyu Chen, Yi Zhang, Jun Zhao
Publication date: 14 June 2018
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2018.02.006
Related Items (17)
Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity ⋮ High-dimensional expectile regression incorporating graphical structure among predictors ⋮ Variable selection in high-dimensional linear model with possibly asymmetric errors ⋮ A new GEE method to account for heteroscedasticity using asymmetric least-square regressions ⋮ Expectile regression for spatial functional data analysis (sFDA) ⋮ Multiple change-points estimation in linear regression models via an adaptive Lasso expectile loss function ⋮ Real-time detection of a change-point in a linear expectile model ⋮ An improved algorithm for high-dimensional continuous threshold expectile model with variance heterogeneity ⋮ Variable selection and debiased estimation for single‐index expectile model ⋮ Automatic selection by penalized asymmetric L q -norm in a high-dimensional model with grouped variables ⋮ Partially Linear Expectile Regression Using Local Polynomial Fitting ⋮ Parametric expectile regression and its application for premium calculation ⋮ Expectile trace regression via low-rank and group sparsity regularization ⋮ The functional \(k\mathrm{NN}\) estimator of the conditional expectile: uniform consistency in number of neighbors ⋮ An elastic-net penalized expectile regression with applications ⋮ Local linear estimate of the functional expectile regression ⋮ Nonparametric estimation of expectile regression in functional dependent data
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Asymmetric Least Squares Estimation and Testing
- Geoadditive expectile regression
- High-dimensional generalizations of asymmetric least squares regression and their applications
- Optimal expectile smoothing
- Heuristics of instability and stabilization in model selection
- Solving a class of linearly constrained indefinite quadratic problems by DC algorithms
- Nonconcave penalized likelihood with a diverging number of parameters.
- Adaptive robust variable selection
- On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function
- The Concave-Convex Procedure
- 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?
- Nonconcave Penalized Likelihood With NP-Dimensionality
- Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
- Smoothly Clipped Absolute Deviation on High Dimensions
This page was built for publication: Expectile regression for analyzing heteroscedasticity in high dimension
