Evidence map›Paper›PMID 41001515›Full record

ArticleJournal of the American Statistical Association2025

Robustifying Likelihoods by Optimistically Re-weighting Data.

Miheer Dewaskar, Christopher Tosh, Jeremias Knoblauch, David B Dunson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Miheer DewaskarDepartment of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87106.
Christopher ToshDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065.
Jeremias KnoblauchDepartment of Statistics, UCL, London WC1E7HB, UK.
David B DunsonDepartment of Statistical Science, Duke University, Durham, NC 27705.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
The Center for Tumor-Immune Systems Biology at MSKCCU54CA274492 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI Christina S Leslie · 2022 to 2026
$16.3M
Reproducibility and Robustness of Dimensionality ReductionR01ES027498 · NIEHS · DUKE UNIVERSITY · PI HERRING, AMY H · 2017 to 2021
$2.9M
Probabilistic Multiscale Modeling of the Tumor MicroenvironmentR37CA271186 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI Wesley Tansey · 2023 to 2026
$2.7M
Improving inferences on health effects of chemical exposuresR01ES035625 · NIEHS · DUKE UNIVERSITY · PI David Brian Dunson · 2023 to 2026
$1.6M
NCI NIH HHS P30 CA008748NCI NIH HHS R37 CA271186NCI NIH HHS U54 CA274492NIEHS NIH HHS R01 ES027498NIEHS NIH HHS R01 ES035625
6 · The paper itself

Abstract

Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in selecting a good model for the data at hand, there is inevitably some amount of model misspecification: outliers, data contamination or inappropriate parametric assumptions such as Gaussianity mean that most models are at best rough approximations of reality. A significant practical concern is that for certain inferences, even small amounts of model misspecification may have a substantial impact; a problem we refer to as

Indexed as

Coarsened BayesData contaminationMixture modelsModel misspecificationOutliersRobust inferenceTotal variation distance

Identifiers

PMID41001515
PMCPMC12459652

What OpenQuestion holds

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Registered trials

None linked

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.