Evidence map›Paper›PMID 34824523›Full record

ArticleStatistica Sinica2020

A New Semiparametric Approach to Finite Mixture of Regressions using Penalized Regression via Fusion.

Erin Austin, Wei Pan, Xiaotong Shen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistica Sinica, 2020. 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. Health Care Provider Clustering Using Fusion Penalty in Quasi-Likelihood.Biometrical journal. Biometrische Zeitschrift · 2024
    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

3 authors.

Erin AustinDepartment of Mathematical and Statistical Sciences, University of Colorado Denver, 80204.
Wei PanDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455.
Xiaotong ShenSchool of Statistics, University of Minnesota, Minneapolis, MN 55455.

Funding

Powerful Inference and Prediction for Genetic AssociationR01HL105397 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2011 to 2018
$2.9M
New Machine Learning Tools for Biomedical DataR01GM081535 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2007 to 2014
$2.2M
Statistical Methods for Genomic and Proteomic DataR01HL065462 · NHLBI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI PAN, WEI · 2002 to 2012
$1.5M
Estimation and Inference of Gene Regulatory NetworksR01GM126002 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2017 to 2020
$1.4M
NHLBI NIH HHS R01 HL065462NHLBI NIH HHS R01 HL105397NIGMS NIH HHS R01 GM081535NIGMS NIH HHS R01 GM126002
6 · The paper itself

Abstract

For some modeling problems a population may be better assessed as an aggregate of unknown subpopulations, each with a distinct relationship between a response and associated variables. The finite mixture of regressions (FMR) model, in which an outcome is derived from one of a finite number of linear regression models, is a natural tool in this setting. In this article, we first propose a new penalized regression approach. Then, we demonstrate how the proposed approach better identifies subpopulations and their corresponding models than a semiparametric FMR method does. Our new method fits models for each person via grouping pursuit, utilizing a new group-truncated

Indexed as

FMRGrouping PursuitGroup LASSOGroup TLPPenalized RegressionSemiparametric

Identifiers

PMID34824523
PMCPMC8612088

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.