gss
Smoothing spline ANOVA models 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.
(Source: http://cran.r-project.org/web/packages)
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References in zbMATH (referenced in 282 articles , 3 standard articles )
Showing results 1 to 20 of 282.
Sorted by year (- Barrientos, Andrés F.; Canale, Antonio: A Bayesian goodness-of-fit test for regression (2021)
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- Kounchev, O.; Render, H.: Error estimates for interpolation with piecewise exponential splines of order two and four (2021)
- Li, Wenyu; Hegde, Arun; Oreluk, James; Packard, Andrew; Frenklach, Michael: Representing model discrepancy in bound-to-bound data collaboration (2021)
- Sun, Xiaoxiao; Zhong, Wenxuan; Ma, Ping: An asymptotic and empirical smoothing parameters selection method for smoothing spline ANOVA models in large samples (2021)
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- Gao, Zhenguo; Du, Pang; Jin, Ran; Robertson, John L.: Surface temperature monitoring in liver procurement via functional variance change-point analysis (2020)
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- Kounchev, O.; Render, H.; Tsachev, T.: On a class of (L)-splines of order 4: fast algorithms for interpolation and smoothing (2020)
- Lamboni, Matieyendou: Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices (2020)
- Li, Ting; Zhu, Zhongyi: Inference for generalized partial functional linear regression (2020)
- Liu, Meimei; Shang, Zuofeng; Cheng, Guang: Nonparametric distributed learning under general designs (2020)
- Liu, Yanyan; Mao, Guangcai; Zhao, Xingqiu: Local asymptotic inference for nonparametric regression with censored survival data (2020)
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- Sung, Chih-Li; Wang, Wenjia; Plumlee, Matthew; Haaland, Benjamin: Multiresolution functional ANOVA for large-scale, many-input computer experiments (2020)
- Sun, Jinhui; Du, Pang; Miao, Hongyu; Liang, Hua: Robust feature screening procedures for single and mixed types of data (2020)
- Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH