Smoothing Spline ANOVA Models

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Smoothing Spline ANOVA Models by Chong Gu, Springer New York
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Chong Gu ISBN: 9781461453697
Publisher: Springer New York Publication: January 26, 2013
Imprint: Springer Language: English
Author: Chong Gu
ISBN: 9781461453697
Publisher: Springer New York
Publication: January 26, 2013
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer New York

Cover of the book Glutamine in Clinical Nutrition by Chong Gu
Cover of the book Cobalt Blues by Chong Gu
Cover of the book Handbook of Family Policies Across the Globe by Chong Gu
Cover of the book Identifying, Assessing, and Treating Early Onset Schizophrenia at School by Chong Gu
Cover of the book So You Want a Meade LX Telescope! by Chong Gu
Cover of the book Systems Design for Remote Healthcare by Chong Gu
Cover of the book From Antarctica to Outer Space by Chong Gu
Cover of the book History of Psychology in Autobiography by Chong Gu
Cover of the book Recent Trends in Theoretical Psychology by Chong Gu
Cover of the book Terrorism Within Comparative International Context by Chong Gu
Cover of the book Synaptic Plasticity in Pain by Chong Gu
Cover of the book Dynamics On and Of Complex Networks, Volume 2 by Chong Gu
Cover of the book Ecoregions by Chong Gu
Cover of the book Mapping Archaeological Landscapes from Space by Chong Gu
Cover of the book Future Professional Communication in Astronomy II by Chong Gu
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy