What question did this study set out to answer?

To develop an efficient and robust transfer learning framework for high-dimensional quantile regression by combining a smooth Huber approximation with elastic net regularization.

August 1, 2026Statistical Analysis and Data Mining The ASA Data Science Journal

Transfer Learning for High‐Dimensional Huber Approximate Quantile Regression via Elastic Net

View Full Paper

Ask AI

Bookmark

Authors

ZLZixin Lv YLYichen Liu KMKang Meng

Discussion

Member takes

Implication

Methodological study demonstrates improved gene expression prediction accuracy in genomic data, highlighting its utility for modeling heterogeneous biological effects.

Key Points

To develop an efficient and robust transfer learning framework for high-dimensional quantile regression by combining a smooth Huber approximation with elastic net regularization.
Replaced the non-differentiable quantile check function with a smooth Huber loss function to achieve computational efficiency and robustness against heavy-tailed errors and outliers.
Developed Oracle Trans-HAQ using elastic net penalties for sparse source knowledge integration, alongside THAQ, a cross-validation framework to prevent negative transfer.
Evaluated performance using high-dimensional simulation studies and real-world genomic data from the Genotype-Tissue Expression (GTEx) project predicting JAM2 gene expression across brain tissues.
Achieved significantly lower estimation errors in numerical simulations compared to convolution-smoothed quantile regression and pure quantile loss methods.
Demonstrated improved prediction accuracy for JAM2 gene expression quantiles across heterogeneous human brain tissue samples.

Cite This Study

Lv et al. (2026) studied this question.

synapsesocial.com/papers/6a8ee8037ed0ac39980001ce https://doi.org/10.1002/sam.70111

View Full Paper

Ask AI

Bookmark

August 1, 2026Statistical Analysis and Data Mining The ASA Data Science Journal

Transfer Learning for High‐Dimensional Huber Approximate Quantile Regression via Elastic Net

View Full Paper

Ask AI

Bookmark

Authors

ZLZixin Lv YLYichen Liu KMKang Meng

Discussion

Member takes

Implication

Methodological study demonstrates improved gene expression prediction accuracy in genomic data, highlighting its utility for modeling heterogeneous biological effects.

Key Points

To develop an efficient and robust transfer learning framework for high-dimensional quantile regression by combining a smooth Huber approximation with elastic net regularization.
Replaced the non-differentiable quantile check function with a smooth Huber loss function to achieve computational efficiency and robustness against heavy-tailed errors and outliers.
Developed Oracle Trans-HAQ using elastic net penalties for sparse source knowledge integration, alongside THAQ, a cross-validation framework to prevent negative transfer.
Evaluated performance using high-dimensional simulation studies and real-world genomic data from the Genotype-Tissue Expression (GTEx) project predicting JAM2 gene expression across brain tissues.
Achieved significantly lower estimation errors in numerical simulations compared to convolution-smoothed quantile regression and pure quantile loss methods.
Demonstrated improved prediction accuracy for JAM2 gene expression quantiles across heterogeneous human brain tissue samples.

Cite This Study

Lv et al. (2026) studied this question.

synapsesocial.com/papers/6a8ee8037ed0ac39980001ce https://doi.org/10.1002/sam.70111

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark