Yuedong Wang

Smoothing Splines (eBook, ePUB)

Methods and Applications

• Format: ePub

Produktbeschreibung

With many real-world examples, this book shows how to apply the powerful methods of smoothing splines in practice. It covers basic smoothing spline models as well as more advanced models, such as spline smoothing with correlated random errors. It also presents methods for model selection and inference. The author makes the advanced smoothing spline methodology based on reproducing kernel Hilbert space (RKHS) accessible to practitioners and students by keeping theory to a minimum. R is used throughout to implement the methods, with code available for download on the book's web page.

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Produktdetails

• Verlag: Taylor & Francis
• Erscheinungstermin: 22. Juni 2011
• Englisch
• ISBN-13: 9781040070307
• Artikelnr.: 72519121

Autorenporträt

Yuedong Wang is a professor and the chair of the Department of Statistics and Applied Probability at the University of California-Santa Barbara. Dr. Wang is an elected fellow of the ASA and ISI, a fellow of the RSS, and a member of IMS, IBS, and ICSA. His research covers the development of statistical methodology and its applications.

Inhaltsangabe

Introduction. Smoothing Spline Regression. Smoothing Parameter Selection
and Inference. Smoothing Spline ANOVA. Spline Smoothing with
Heteroscedastic and/or Correlated Errors. Generalized Smoothing Spline
ANOVA. Smoothing Spline Nonlinear Regression. Semiparametric Regression.
Semiparametric Mixed-Effects Models. Appendices. References. Indices.