Show simple item record

dc.contributor.author
Aeberhard, William H.
dc.contributor.author
Cantoni, Eva
dc.contributor.author
Marra, Giampiero
dc.contributor.author
Radice, Rosalba
dc.date.accessioned
2021-01-22T08:49:54Z
dc.date.available
2021-01-22T03:49:47Z
dc.date.available
2021-01-22T08:49:54Z
dc.date.issued
2021-01-12
dc.identifier.issn
0960-3174
dc.identifier.issn
1573-1375
dc.identifier.other
10.1007/s11222-020-09979-x
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/464572
dc.identifier.doi
10.3929/ethz-b-000464572
dc.description.abstract
The validity of estimation and smoothing parameter selection for the wide class of generalized additive models for location, scale and shape (GAMLSS) relies on the correct specification of a likelihood function. Deviations from such assumption are known to mislead any likelihood-based inference and can hinder penalization schemes meant to ensure some degree of smoothness for nonlinear effects. We propose a general approach to achieve robustness in fitting GAMLSSs by limiting the contribution of observations with low log-likelihood values. Robust selection of the smoothing parameters can be carried out either by minimizing information criteria that naturally arise from the robustified likelihood or via an extended Fellner–Schall method. The latter allows for automatic smoothing parameter selection and is particularly advantageous in applications with multiple smoothing parameters. We also address the challenge of tuning robust estimators for models with nonlinear effects by proposing a novel median downweighting proportion criterion. This enables a fair comparison with existing robust estimators for the special case of generalized additive models, where our estimator competes favorably. The overall good performance of our proposal is illustrated by further simulations in the GAMLSS setting and by an application to functional magnetic resonance brain imaging using bivariate smoothing splines.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Bounded influence function
en_US
dc.subject
Nonparametric regression
en_US
dc.subject
Penalized smoothing splines
en_US
dc.subject
Robust smoothing parameter selection
en_US
dc.subject
Robust information criterion
en_US
dc.title
Robust fitting for generalized additive models for location, scale and shape
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Statistics and Computing
ethz.journal.volume
31
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Stat. comput.
ethz.pages.start
11
en_US
ethz.size
16 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-01-22T03:49:57Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-01-22T08:50:09Z
ethz.rosetta.lastUpdated
2021-02-15T23:29:22Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Robust%20fitting%20for%20generalized%20additive%20models%20for%20location,%20scale%20and%20shape&rft.jtitle=Statistics%20and%20Computing&rft.date=2021-01-12&rft.volume=31&rft.issue=1&rft.spage=11&rft.issn=0960-3174&1573-1375&rft.au=Aeberhard,%20William%20H.&Cantoni,%20Eva&Marra,%20Giampiero&Radice,%20Rosalba&rft.genre=article&rft_id=info:doi/10.1007/s11222-020-09979-x&
 Search print copy at ETH Library

Files in this item

Thumbnail

Publication type

Show simple item record