Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing parameter selection in semiparametric regression. However, existing REML or marginal likelihood (ML) based methods for semipar...

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Bibliographic Details
Published in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2011-01, Vol.73 (1), p.3-36
Main Author:	Wood, Simon N.
Format:	Article
Language:	English
Subjects:	Adaptive smoothing Approximation Convergence Criteria Data smoothing Differentiation Eigenvalues Estimation Estimation methods Generalized additive mixed model Generalized additive model Generalized cross-validation Generalized linear model Generalized linear models Grammatical aspect Information Linear analysis Linear models Linear regression Marginal likelihood Mathematical methods Maximum likelihood estimation Maximum likelihood method Methodology Methods Model selection Modeling Octane ratings Optimization Owls Parameter estimation Parametric models Penalized generalized linear model Penalized regression splines prediction Probability Property Restricted maximum likelihood Robustness Scalar on function regression Scalarity (Semantics) Scores selection methods Simulation Smoothing Stable computation Statistical analysis Statistical methods Statistics Studies wood
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