Hard Landing Analysis by Transfer Learning for High-Dimensional Quantile Regression

• Activity Number: 419 - Section on Risk Analysis Student Paper Award Session
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Jun Jin* and Kun Chen and Jun Yan
• Companies: University of Connecticut and University of Connecticut and University of Connecticut
• Keywords: Target learning; sparsity quantile regression; flight safety

Abstract

In this paper, we design the transfer learning methods for the high-dimensional quantile regression (QR), utilizing the different QR models built on the source populations to improve the performance of QR on the target population. Given certain informative sources, the oracle transfer learning quantile regression is carried out with its l2-estimation error bound and l2-prediction error bound. When the informative sources are unknown, to make inferences on the index of informative sources, a cross-validation-based informative source detection procedure is introduced. The group theoretically proves that under mild conditions, the detection is consistent, and further, the transfer learning QR enjoys a sharper convergence rate than the QR with only the target dataset while circumvents the negative learning problem. Groups of simulations verify this improvement of performance. Real-data analysis on the hard landing risk detection for flight safety is implemented with the proposed transfer learning method, proved with better performance by the cross-validation.