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- Title
Identifiability constraints in generalized additive models.
- Authors
Stringer, Alex
- Abstract
Identifiability constraints are necessary for parameter estimation when fitting models with nonlinear covariate associations. The choice of constraint affects standard errors of the estimated curve. Centring constraints are often applied by default because they are thought to yield lowest standard errors out of any constraint, but this claim has not been investigated. We show that whether centring constraints are optimal depends on the response distribution and parameterization, and that for natural exponential family responses under the canonical parametrization, centring constraints are optimal only for Gaussian response.
- Subjects
EXPONENTIAL families (Statistics); PARAMETER estimation; NONLINEAR regression; PARAMETERIZATION
- Publication
Canadian Journal of Statistics, 2024, Vol 52, Issue 2, p461
- ISSN
0319-5724
- Publication type
Article
- DOI
10.1002/cjs.11786