Nonparametric estimators over metric graphs
Authors
Research Topics
Paper Information
-
Journal:
Biometrika -
DOI:
10.1093/biomet/asag029 -
Published:
April 15, 2026 -
Added to Tracker:
Apr 16, 2026
Abstract
Abstract This work discusses a theory of functional spaces over metric graphs that permits the definition of penalized likelihood methods for data observed over spatial supports that are graphs. Within the considered mathematical framework, we recover classical results in functional analysis, such as a Poincaré-type inequality. This, in turn, enables us to uplift, to the present setting, the theory of some fundamental penalized likelihood methods. Specifically, we present two important classes of statistical models: nonparametric regression and nonparametric density estimation, here defined for data observed over graphs. We derive theoretical results regarding the well-posedness of the associated estimation problems and the consistency of the estimators. We also demonstrate the performance of the defined estimators with respect to state-of-the-art alternatives.
Author Details
Aldo Clemente
AuthorEleonora Arnone
AuthorJorge Mateu
AuthorLaura M Sangalli
AuthorResearch Topics & Keywords
Nonparametric Statistics
Research AreaCitation Information
APA Format
Aldo Clemente
,
Eleonora Arnone
,
Jorge Mateu
&
Laura M Sangalli
(2026)
.
Nonparametric estimators over metric graphs.
Biometrika
, 10.1093/biomet/asag029.
BibTeX Format
@article{paper1118,
title = { Nonparametric estimators over metric graphs },
author = {
Aldo Clemente
and Eleonora Arnone
and Jorge Mateu
and Laura M Sangalli
},
journal = { Biometrika },
year = { 2026 },
doi = { 10.1093/biomet/asag029 },
url = { https://doi.org/10.1093/biomet/asag029 }
}