Evidence map›Paper›PMID 32454528›Full record

ArticleBiometrika2020

Adaptive nonparametric regression with the

Oscar Hernan Madrid Padilla, James Sharpnack, Yanzhen Chen, Daniela M Witten

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.2field-weighted citation impact, top 11% of its field
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

3 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. High-dimensional partial linear model with trend filtering.Electronic journal of statistics · 2026
    Article
  3. 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 at 4 institutions in 2 countries.

Oscar Hernan Madrid PadillaDepartment of Statistics, University of California, 520 Portola Plaza, Los Angeles, California, U.S.A.
James SharpnackDepartment of Statistics, University of California, One Shields Avenue, Davis, California, U.S.A.
Yanzhen ChenDepartment of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
Daniela M WittenDepartment of Statistics, University of Washington, Seattle, Washington, U.S.A.
Hong Kong University of Science and Technology · HKUniversity of California, Davis · USUniversity of California, Los Angeles · USUniversity of Washington · US

Funding

High-dimensional unsupervised learning, with applications to genomicsDP5OD009145 · OD · UNIVERSITY OF WASHINGTON · PI WITTEN, DANIELA · 2011 to 2015
$1.8M
NIH HHS DP5 OD009145
6 · The paper itself

Abstract

The fused lasso, also known as total-variation denoising, is a locally adaptive function estimator over a regular grid of design points. In this article, we extend the fused lasso to settings in which the points do not occur on a regular grid, leading to a method for nonparametric regression. This approach, which we call the [Formula: see text]-nearest-neighbours fused lasso, involves computing the [Formula: see text]-nearest-neighbours graph of the design points and then performing the fused lasso over this graph. We show that this procedure has a number of theoretical advantages over competing methods: specifically, it inherits local adaptivity from its connection to the fused lasso, and it inherits manifold adaptivity from its connection to the [Formula: see text]-nearest-neighbours approach. In a simulation study and an application to flu data, we show that excellent results are obtained. For completeness, we also study an estimator that makes use of an [Formula: see text]-graph rather than a [Formula: see text]-nearest-neighbours graph and contrast it with the [Formula: see text]-nearest-neighbours fused lasso.

Indexed as

Fused lassoLocal adaptivityManifold adaptivityNonparametric regressionTotal variation

Identifiers

PMID32454528
PMCPMC7228543
OpenAlexW3003653105

What OpenQuestion holds

Textmetadata
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.