Evidence map›Paper›PMID 31182883›Full record

ArticleJournal of multivariate analysis2018

Variable selection for partially linear models via partial correlation.

Jingyuan Liu, Lejia Lou, Runze Li

Abstract read
In one paragraph

Article in Journal of multivariate analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Jingyuan LiuDepartment of Statistics in School of Economics, Wang Yanan Institute for Studies in Economics and Fujian Key Laboratory of Statistical Science, Xiamen University, Xiamen, Fujian, 361005, China.
Lejia LouErnst & Young, 5 Times Square, New York, NY 10036 USA.
Runze LiDepartment of Statistics and The Methodology Center, The Pennsylvania State University, University Park, PA 16802 USA.

Funding

Research Training and Educational Component (Component I) p370-385P50DA036107 · NIDA · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI FOULDS, JONATHAN, MUSCAT, JOSHUA E · 2013 to 2018
$19.5M
Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI COLLINS, LINDA M, LANZA, STEPHANIE T · 2015 to 2019
$13.9M
NIDA NIH HHS P50 DA036107NIDA NIH HHS P50 DA039838
6 · The paper itself

Abstract

The partially linear model (PLM) is a useful semiparametric extension of the linear model that has been well studied in the statistical literature. This paper proposes a variable selection procedure for the PLM with ultrahigh dimensional predictors. The proposed method is different from the existing penalized least squares procedure in that it relies on partial correlation between the partial residuals of the response and the predictors. We systematically study the theoretical properties of the proposed procedure and prove its model consistency property. We further establish the root-

Indexed as

Model selection consistencypartial faithfulnesssemiparametric regression modeling

Identifiers

PMID31182883
PMCPMC6555488

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.