Nonparametric Neighborhood Selection in Graphical Models.

Hao Dong, Yuedong Wang

Abstract read

In one paragraph

Article in Journal of machine learning research : JMLR, 2022. 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.

Detection of Interaction Effects in a Nonparametric Concurrent Regression Model.Entropy (Basel, Switzerland) · 2023
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

2 authors.

Hao DongDepartment of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA, USA.

Yuedong WangDepartment of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA, USA.

Funding

Early detection, containment, and management of COVID-19 in dialysis facilities using multi-modal data sourcesR01DK130067 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI GUO, WENSHENG, KOTANKO, PETER · 2021 to 2023

$2.0M

NIDDK NIH HHS R01 DK130067

6 · The paper itself

Abstract

The neighborhood selection method directly explores the conditional dependence structure and has been widely used to construct undirected graphical models. However, except for some special cases with discrete data, there is little research on nonparametric methods for neighborhood selection with mixed data. This paper develops a fully nonparametric neighborhood selection method under a consolidated smoothing spline ANOVA (SS ANOVA) decomposition framework. The proposed model is flexible and contains many existing models as special cases. The proposed method provides a unified framework for mixed data without any restrictions on the type of each random variable. We detect edges by applying an

Indexed as

conditional density estimationmixed dataregularizationreproducing kernel Hilbert spacesmoothing spline ANOVA

Identifiers

PMID41415627

PMCPMC12711317

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.