Evidence map›Paper›PMID 40566144›Full record

ArticleEntropy (Basel, Switzerland)2025

Sinkhorn Distributionally Robust Conditional Quantile Prediction with Fixed Design.

Guohui Jiang, Tiantian Mao

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Guohui JiangDepartment of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230052, China.
Tiantian MaoDepartment of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230052, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a novel data-driven distributionally robust framework for conditional quantile prediction under the fixed design setting of the covariates, which we refer to as Sinkhorn distributionally robust conditional quantile prediction. We derive a convex programming dual reformulation of the proposed problem and further develop a conic optimization reformulation for the case with finite support. Our method's superior performance is demonstrated through several numerical experiments, highlighting its effectiveness in practical applications.

Indexed as

conditional quantile predictiondistributionally robust optimizationSinkhorn distance

Identifiers

PMID40566144
PMCPMC12192490

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.