Handling multicollinearity in quantile regression through the use of principal component regression
From MaRDI portal
Publication:2168550
DOI10.1007/s40300-022-00230-3OpenAlexW4213003039MaRDI QIDQ2168550
Domenico Vistocco, Cristina Davino, Rosaria Romano
Publication date: 31 August 2022
Published in: Metron (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s40300-022-00230-3
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- One-step sparse estimates in nonconcave penalized likelihood models
- The effect of school quality on student performance: A quantile regression approach
- Principal component analysis.
- Quantile regression for longitudinal data
- Quantile regression models with factor‐augmented predictors and information criterion
- Regresion analysis with multicollinear predictor variables: definition, derection, and effects
- Robust Tests for Heteroscedasticity Based on Regression Quantiles
- Regression Quantiles
- Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression
- Comparison of Prediction Methods When Only a Few Components are Relevant
- Goodness of Fit and Related Inference Processes for Quantile Regression
- An Introduction to Statistical Learning
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Quantile Regression
- Variable selection in quantile regression via Gibbs sampling
- Regularization and Variable Selection Via the Elastic Net
This page was built for publication: Handling multicollinearity in quantile regression through the use of principal component regression