Evidence map›Paper›PMID 40099197›Full record

ArticleStatistics and computing2025

Estimation and model selection for finite mixtures of Tukey's

Tingting Zhan, Misung Yi, Amy R Peck, Hallgeir Rui, Inna Chervoneva

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics and computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

5 authors.

Tingting ZhanDivision of Biostatistics and Bioinformatics, Department of Pharmacology, Physiology and Cancer Biology, Thomas Jefferson University, 130 S. 9th Street, 17th Floor, Philadelphia, PA 19107 USA.
Misung YiDivision of Biostatistics and Bioinformatics, Department of Pharmacology, Physiology and Cancer Biology, Thomas Jefferson University, 130 S. 9th Street, 17th Floor, Philadelphia, PA 19107 USA.
Amy R PeckDivision of Cancer Biology, Department of Pharmacology, Physiology and Cancer Biology, Thomas Jefferson University, Philadelphia, USA.
Hallgeir RuiDivision of Cancer Biology, Department of Pharmacology, Physiology and Cancer Biology, Thomas Jefferson University, Philadelphia, USA.
Inna ChervonevaDivision of Biostatistics and Bioinformatics, Department of Pharmacology, Physiology and Cancer Biology, Thomas Jefferson University, 130 S. 9th Street, 17th Floor, Philadelphia, PA 19107 USA.

Funding

WNT pathway-driven anti-estrogen therapy resistance in breast cancerR01CA267549 · NCI · THOMAS JEFFERSON UNIVERSITY · PI Inna Chervoneva, Hallgeir Rui · 2023 to 2026
$2.2M
Statistical Methods For Quantitative Immunohistochemistry BiomarkersR01CA222847 · NCI · THOMAS JEFFERSON UNIVERSITY · PI CHERVONEVA, INNA · 2019 to 2023
$1.8M
NCI NIH HHS R01 CA222847NCI NIH HHS R01 CA267549
6 · The paper itself

Abstract

A finite mixture of distributions is a popular statistical model, which is especially meaningful when the population of interest may include distinct subpopulations. This work is motivated by analysis of protein expression levels quantified using immunofluorescence immunohistochemistry assays of human tissues. The distributions of cellular protein expression levels in a tissue often exhibit multimodality, skewness and heavy tails, but there is a substantial variability between distributions in different tissues from different subjects, while some of these mixture distributions include components consistent with the assumption of a normal distribution. To accommodate such diversity, we propose a mixture of 4-parameter Tukey's

Indexed as

Cellular protein levelFinite mixturesIndirect estimatorQuantile least Mahalanobis distanceTukey’s g- &-h distribution

Identifiers

PMID40099197
PMCPMC11910465

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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.