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Related Experiment Video

Updated: Sep 18, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Sinkhorn Distributionally Robust Conditional Quantile Prediction with Fixed Design.

Guohui Jiang¹, Tiantian Mao¹

¹Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230052, China.

Entropy (Basel, Switzerland)

|June 26, 2025

Summary

This study introduces a new data-driven method for conditional quantile prediction, called Sinkhorn distributionally robust conditional quantile prediction. It offers superior performance in practical applications, validated by numerical experiments.

Keywords:

Sinkhorn distance conditional quantile prediction distributionally robust optimization

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Area of Science:

Statistics
Machine Learning
Optimization

Background:

Conditional quantile prediction is crucial for understanding risk and uncertainty.
Existing methods may lack robustness to distributional shifts.
Data-driven approaches are increasingly important in statistical modeling.

Purpose of the Study:

To propose a novel distributionally robust framework for conditional quantile prediction.
To address the fixed design setting of covariates.
To develop an efficient computational approach for the proposed method.

Main Methods:

A data-driven distributionally robust framework is proposed.
The framework is termed Sinkhorn distributionally robust conditional quantile prediction.
Convex programming dual reformulation and conic optimization reformulation are derived.

Main Results:

The proposed method demonstrates superior performance compared to existing approaches.
Numerical experiments validate the effectiveness of the framework.
The method is highlighted for its practical applicability.

Conclusions:

The Sinkhorn distributionally robust conditional quantile prediction framework is effective.
The derived reformulations enable efficient computation.
The approach offers a robust solution for conditional quantile estimation.