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Cited by 315Statistical Methods and InferenceStatistical Methods and ApplicationsAdvanced Statistical Methods and Models

Shape constrained additive models

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Pya, Natalya, Wood, Simon N.·2015·Statistics and Computing
DOI 10.1007/s11222-013-9448-7
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Abstract

A framework is presented for generalized additive modelling under shape constraints on the component functions of the linear predictor of the GAM. We represent shape constrained model components by mildly non-linear extensions of P-splines. Models can contain multiple shape constrained and unconstrained terms as well as shape constrained multi-dimensional smooths. The constraints considered are on the sign of the first or/and the second derivatives of the smooth terms. A key advantage of the approach is that it facilitates efficient estimation of smoothing parameters as an integral part of model estimation, via GCV or AIC, and numerically robust algorithms for this are presented. We also derive simulation free approximate Bayesian confidence intervals for the smooth components, which are shown to achieve close to nominal coverage probabilities. Applications are presented using real data examples including the risk of disease in relation to proximity to municipal incinerators and the association between air pollution and health.

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  • The study categorized hematopoietic transcription factors of Library A with generalized additive models under shape constraints.

    “We further categorized the hematopoietic TFs of Library A with generalized additive models under shape constraints92 (R package scam).”

    From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [92]

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Authors
Pya, N., & Wood, S. N.
Affiliations
University of Bath (GB)
Corresponding author
Pya, Natalya
ORCID
Wood, Simon N.
Venue
Statistics and Computing
Publisher
Springer Science and Business Media LLC
Published
2014-02-25
Volume
25
Issue
3
Pages
543-559
ISSN
  • 0960-3174 (ISSN-L)
  • 1573-1375
DOI
10.1007/s11222-013-9448-7
OpenAlex
W2059940278
Cited by
315
References
2
Citations by year
2026: 53 · 2025: 31 · 2024: 40 · 2023: 24 · 2022: 31 · 2021: 38 · 2020: 28 · 2019: 17 · 2018: 26 · 2017: 9 · 2016: 8 · 2015: 7 · 2014: 3
FWCI
9.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Statistics and Computing (publishedVersion, cc-by)
  • Explore Bristol Research (publishedVersion, cc-by)
  • Bristol Research (University of Bristol) (submittedVersion, cc-by)
  • KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
Funders
  • Department of Health, Northern Ireland
  • Health and Safety Executive
  • Engineering and Physical Sciences Research Council (EP/I000917/1, EP/1000917/1, EP/K005251/1)
  • Natural Environment Research Council (EP/1000917/1)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Keywords
generalized additive models, P-splines, convexity constraints, smoothing parameter estimation, generalized cross-validation, Akaike information criterion, air pollution and health

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