Identifiability constraints in generalized additive models
نویسندگان
چکیده
Abstract Identifiability constraints are necessary for parameter estimation when fitting models with nonlinear covariate associations. The choice of constraint affects standard errors the estimated curve. Centring often applied by default because they thought to yield lowest out any constraint, but this claim has not been investigated. We show that whether centring optimal depends on response distribution and parameterization, natural exponential family responses under canonical parametrization, only Gaussian response.
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ژورنال
عنوان ژورنال: Canadian journal of statistics
سال: 2023
ISSN: ['0319-5724', '1708-945X']
DOI: https://doi.org/10.1002/cjs.11786