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130109s2002 nyu o 000 0 eng |
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20260420212319.1 |
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|a 9781475736830 (electronic bk.)
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|a 1475736835 (electronic bk.)
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|z 9781441929662
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|a 519.5
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|a Gu, Chong.
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| 245 |
1 |
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|a Smoothing Spline ANOVA Models /
|c by Chong Gu.
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| 264 |
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1 |
|a New York, NY :
|b Springer New York,
|c 2002.
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| 300 |
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|a 1 online resource (xiii, 290 pages)
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|a text
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|a online resource
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| 490 |
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|a Springer series in statistics,
|x 0172-7397
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| 505 |
0 |
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|a Introduction -- Model Construction -- Regression with Gaussian Type Responses -- More Splines -- Regression with Exponential Families -- Probability Density Estimation -- Hazard Rate Estimation -- Asymptotic Convergence.
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| 520 |
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|a 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.
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|a Electronic resource.
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|a Statistics.
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| 650 |
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|a Mathematical statistics.
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| 650 |
|
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|a Mathematical statistics.
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| 650 |
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|a Statistics.
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|i Print version:
|z 9781441929662
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| 830 |
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|a Springer series in statistics.
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| 856 |
4 |
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|u http://proxy.library.tamu.edu/login?url=https://link.springer.com/10.1007/978-1-4757-3683-0
|z Connect to the full text of this electronic book
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|a Texas A&M University
|b College Station
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|h Library of Congress classification
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| 998 |
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