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 Macromolecular Anticancer Therapeutics by Chong Gu
Cover of the book Numerical Ecology with R by Chong Gu
Cover of the book Argillaceous Rock Atlas by Chong Gu
Cover of the book Imagery and Cognition by Chong Gu
Cover of the book Dynamic Reconfiguration in Real-Time Systems by Chong Gu
Cover of the book Reviews of Environmental Contamination and Toxicology by Chong Gu
Cover of the book Perioperative Kidney Injury by Chong Gu
Cover of the book Astronomical Cybersketching by Chong Gu
Cover of the book Residue Reviews by Chong Gu
Cover of the book Fundamental Frequency in Sentence Production by Chong Gu
Cover of the book Systems Analysis of Human Multigene Disorders by Chong Gu
Cover of the book Computational Biology by Chong Gu
Cover of the book Electrothermal Frequency References in Standard CMOS by Chong Gu
Cover of the book Stochastic Orders in Reliability and Risk by Chong Gu
Cover of the book Mathematical Methods and Models in Biomedicine 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