Smoothing Spline ANOVA Models

  • ChongĀ Gu

Part of the Springer Series in Statistics book series (SSS, volume 297)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Chong Gu
    Pages 1-21
  3. Chong Gu
    Pages 23-60
  4. Chong Gu
    Pages 125-173
  5. Chong Gu
    Pages 215-236
  6. Chong Gu
    Pages 237-284
  7. Chong Gu
    Pages 285-318
  8. Chong Gu
    Pages 319-350
  9. Chong Gu
    Pages 351-385
  10. Back Matter
    Pages 387-433

About this book


Nonparametric function estimation with stochastic data, otherwise

known as smoothing, has been studied by several generations of

statisticians. Assisted by the ample computing power in today's

servers, desktops, and laptops, smoothing methods have been finding

their ways into everyday data analysis by practitioners. While scores

of methods have proved successful for univariate smoothing, ones

practical in multivariate settings number far less. Smoothing spline

ANOVA models are a versatile family of smoothing methods derived

through roughness penalties, that are suitable for both univariate and

multivariate problems.

In this book, the author presents a treatise on penalty smoothing

under a unified framework. Methods are developed for (i) regression

with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a

variety of sampling schemes; and (iii) hazard rate estimation with

censored life time data and covariates. The unifying themes are the

general penalized likelihood method and the construction of

multivariate models with built-in ANOVA decompositions. Extensive

discussions are devoted to model construction, smoothing parameter

selection, computation, and asymptotic convergence.


ANOVA ANOVA models Spline smoothing nonparametric smoothing smoothing methods

Authors and affiliations

  • ChongĀ Gu
    • 1
  1. 1.Department of StatisticsPurdue UniversityWest LafayetteUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-5368-0
  • Online ISBN 978-1-4614-5369-7
  • Series Print ISSN 0172-7397
  • About this book