Smoothing Spline ANOVA Models

Smoothing Spline ANOVA Models

by Chong Gu

Paperback(2nd ed. 2013)

$139.99
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Overview

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.

Most of the computational and data analytical tools discussed in the

book are implemented in R, an open-source platform for statistical

computing and graphics. Suites of functions are embodied in the R

package gss, and are illustrated throughout the book using simulated

and real data examples.

This monograph will be useful as a reference work for researchers in

theoretical and applied statistics as well as for those in other

related disciplines. It can also be used as a text for graduate level

courses on the subject. Most of the materials are accessible to a

second year graduate student with a good training in calculus and

linear algebra and working knowledge in basic statistical inferences

such as linear models and maximum likelihood estimates.

Product Details

ISBN-13: 9781489989840
Publisher: Springer New York
Publication date: 06/25/2015
Series: Springer Series in Statistics , #297
Edition description: 2nd ed. 2013
Pages: 433
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Chong Gu received his Ph.D. from University of Wisconsin-Madison in 1989, and has been on the faculty in Department of Statistics, Purdue University since 1990. At various times during his career, he has held visiting appointments at University of British Columbia, University of Michigan, and National Institute of Statistical Sciences.

Table of Contents

Introduction.- Model Construction.- Regression with Gaussian-Type Responses.- More Splines.- Regression and Exponential Families.- Regression with Correlated Responses.- Probability Density Estimation.- Hazard Rate Estimation.- Asymptotic Convergence.- Penalized Pseudo Likelihood.

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