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- KurzbeschreibungNonparametric function estimation with stochastic data, otherwise<br>known as smoothing, has been studied by several generations of<br>statisticians. Assisted by the ample computing power in today's<br>servers, desktops, and laptops, smoothing methods have been finding<br>their ways into everyday data analysis by practitioners. While scores<br>of methods have proved successful for univariate smoothing, ones<br>practical in multivariate settings number far less. Smoothing spline<br>ANOVA models are a versatile family of smoothing methods derived<br>through roughness penalties, that are suitable for both univariate and<br>multivariate problems.<br>In this book, the author presents a treatise on penalty smoothing<br>under a unified framework. Methods are developed for (i) regression<br>with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a<br>variety of sampling schemes; and (iii) hazard rate estimation with<br>censored life time data and covariates. The unifying themes are the<br>general penalized likelihood method and the construction of<br>multivariate models with built-in ANOVA decompositions. Extensive<br>discussions are devoted to model construction, smoothing parameter<br>selection, computation, and asymptotic convergence.<br>Most of the computational and data analytical tools discussed in the<br>book are implemented in R, an open-source platform for statistical<br>computing and graphics. Suites of functions are embodied in the R<br>package gss, and are illustrated throughout the book using simulated<br>and real data examples.<br>This monograph will be useful as a reference work for researchers in<br>theoretical and applied statistics as well as for those in other<br>related disciplines. It can also be used as a text for graduate level<br>courses on the subject. Most of the materials are accessible to a<br>second year graduate student with a good training in calculus and<br>linear algebra and working knowledge in basic statistical inferences<br>such as linear models and maximum likelihood estimates.
- AutorChong Gu
- Ausgabe2nd ed. 2013
- FormatGebundene Ausgabe
- Seiten433 Seiten
- Gewicht836 g
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