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Smoothing Spline ANOVA Models (Springer Series in Statistics) by Chong Gu (auth.) is a nonfiction available to read on EtoBox.

What is Smoothing Spline ANOVA Models (Springer Series in Statistics) about?

Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the recent availability of ample desktop and laptop computing power, smoothing methods are now 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 comprehensive treatment of penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored life time 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 mode

Who reads Smoothing Spline ANOVA Models (Springer Series in Statistics)?

Author

Chong Gu (auth.)

Publisher

Springer New York : Imprint : Springer

Published

2002

Language

EN

ISBN

9781441929662

Category

nonfiction

Subjects

Mathematics, Stem