Variable Selection and Parameter Estimation in Distributed High-Dimensional Quantile Regression with Responses Missing at Random

Key Points

• Findings reveal effective strategies for handling missing data in high-dimensional quantile regression, enhancing model accuracy.
• The analysis shows improvements in parameter estimation under conditions where responses are missing at random.
• Application of variable selection techniques addresses complexities in high-dimensional datasets, streamlining the modeling process.
• Methodological advancements contribute to understanding the impact of missing responses, suggesting enhancements for statistical modeling.