The random effects are generic and are modeled parametrically by assuming that the covariance function depends on a parsimonious set of parameters. These parameters and the smoothing parameter are estimated simultaneously by the generalized maximum likelihood method.
We derive a connection between a nonparametric mixed-effects model and a linear mixed-effects model. This connection suggests a way of fitting a nonparametric mixed-effects model by using existing programs. The classical two-way mixed models and the growth curve models are used as examples to demonstrate how to use smoothing spline ANOVA decompositions to build nonparametric mixed-effects models. Toon meer Toon minder.
Smoothing Splines: Methods and Applications
Researchers of smoothing methods with roughness penalties will likely appreciate this book Sophisticated practitioners may look to the book's examples for modeling ideas and guidance. Instructors of advanced graduate-level statistical modeling courses may want to consider the book as a textbook. Also, instructors in a RKHS-oriented functional analysis course may find the book a valuable reference for applications. The author is one of the few main contributors to the development of this field.
This book presents a comprehensive treatment of penalized likelihood smoothing under a unified framework.
A broad variety of models with ANOVA decompositions is covered for regression, density and hazard rate estimation. I particularly like the case studies a lot, which allow the reader to see the methods work in practice I consider the book to be a good reference for researchers interested in spline smoothing in a general context as it Fried, Metrika, February, The book is well organized in eight clearly written chapters, each of which includes a closing section with useful bibliographic notes and problems for the reader.
Researchers of smoothing methods with roughness penalties will likely appreciate this book. Michael Frey, Technometrics, Vol.
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The bibliographic notes given at the end of each chapter present a good overview of the material contained therein. Most of the computational work is implemented in software which is user-friendly and is available free from the various web sites.
- Variable selection in Bayesian smoothing spline ANOVA models.
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