Adaptive elastic net penalized high-dimensional quantile regression models with generalized coordinate descent algorithm

Key Points

• Quantile regression models improve prediction accuracy across various quantiles, leading to better insights.
• The approach utilizes elastic net penalization with a generalized coordinate descent algorithm for efficiency.
• Assessment reveals that the method excels in handling high-dimensional datasets often found in complex analyses.
• Highlights significant potential for adaptive methods in analyzing relationships in big data, expanding statistical tools.