Smoothed quantile regression for functional partially linear model with ultrahigh-dimensions and censored responses

Key Points

• Improved handling of censored responses leads to more accurate quantile estimation, aligning with high-dimensional data needs.
• The method effectively manages ultrahigh dimensions, showcasing performance improvements in predictive accuracy.
• This analysis utilizes smoothed quantile regression within a functional partially linear model framework, providing robust results.
• Findings suggest better modeling capabilities for complex data structures, highlighting the necessity for advanced statistical techniques.