Smoothing Spline ANOVA Models:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2002
|
| Schriftenreihe: | Springer Series in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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 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 clone of the popular S/S- PLUS language. Code for regression has been distributed in the R package gss freely available through the Internet on CRAN, the Comprehensive R Archive Network. The use of gss facilities is illustrated in the book through simulated and real data examples |
| Beschreibung: | 1 Online-Ressource (XIII, 290 p) |
| ISBN: | 9781475736830 9781441929662 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/978-1-4757-3683-0 |
Internformat
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Datensatz im Suchindex
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| author | Gu, Chong |
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4757-3683-0 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:09Z |
| institution | BVB |
| isbn | 9781475736830 9781441929662 |
| issn | 0172-7397 |
| language | English |
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| spelling | Gu, Chong Verfasser aut Smoothing Spline ANOVA Models by Chong Gu New York, NY Springer New York 2002 1 Online-Ressource (XIII, 290 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 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 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 clone of the popular S/S- PLUS language. Code for regression has been distributed in the R package gss freely available through the Internet on CRAN, the Comprehensive R Archive Network. The use of gss facilities is illustrated in the book through simulated and real data examples Statistics Mathematical statistics Statistical Theory and Methods Statistik Varianzanalyse (DE-588)4187413-4 gnd rswk-swf Glättung (DE-588)4157404-7 gnd rswk-swf Spline (DE-588)4182391-6 gnd rswk-swf Varianzanalyse (DE-588)4187413-4 s Spline (DE-588)4182391-6 s Glättung (DE-588)4157404-7 s 1\p DE-604 https://doi.org/10.1007/978-1-4757-3683-0 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Gu, Chong Smoothing Spline ANOVA Models Statistics Mathematical statistics Statistical Theory and Methods Statistik Varianzanalyse (DE-588)4187413-4 gnd Glättung (DE-588)4157404-7 gnd Spline (DE-588)4182391-6 gnd |
| subject_GND | (DE-588)4187413-4 (DE-588)4157404-7 (DE-588)4182391-6 |
| title | Smoothing Spline ANOVA Models |
| title_auth | Smoothing Spline ANOVA Models |
| title_exact_search | Smoothing Spline ANOVA Models |
| title_full | Smoothing Spline ANOVA Models by Chong Gu |
| title_fullStr | Smoothing Spline ANOVA Models by Chong Gu |
| title_full_unstemmed | Smoothing Spline ANOVA Models by Chong Gu |
| title_short | Smoothing Spline ANOVA Models |
| title_sort | smoothing spline anova models |
| topic | Statistics Mathematical statistics Statistical Theory and Methods Statistik Varianzanalyse (DE-588)4187413-4 gnd Glättung (DE-588)4157404-7 gnd Spline (DE-588)4182391-6 gnd |
| topic_facet | Statistics Mathematical statistics Statistical Theory and Methods Statistik Varianzanalyse Glättung Spline |
| url | https://doi.org/10.1007/978-1-4757-3683-0 |
| work_keys_str_mv | AT guchong smoothingsplineanovamodels |