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)


References in zbMATH (referenced in 238 articles , 3 standard articles )

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  1. Gao, Zhenguo; Shang, Zuofeng; Du, Pang; Robertson, John L.: Variance change point detection under a smoothly-changing mean trend with application to liver procurement (2019)
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  5. Shi, Jian; Liu, Anna; Wang, Yuedong: Spline density estimation and inference with model-based penalties (2019)
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  7. Tan, Zhiqiang; Zhang, Cun-Hui: Doubly penalized estimation in additive regression with high-dimensional data (2019)
  8. Tuo, Rui: Adjustments to computer models via projected kernel calibration (2019)
  9. Wong, Raymond K. W.; Zhang, Xiaoke: Nonparametric operator-regularized covariance function estimation for functional data (2019)
  10. Zhang, Tonglin; Mateu, Jorge: Substationarity for spatial point processes (2019)
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  12. Borgonovo, Emanuele; Buzzard, Gregery T.; Wendell, Richard E.: A global tolerance approach to sensitivity analysis in linear programming (2018)
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  15. Chatla, Suneel Babu; Shmueli, Galit: Efficient estimation of COM-Poisson regression and a generalized additive model (2018)
  16. Chen, Chun-Shu; Huang, Yi-Tsz: Some characteristics on the selection of spline smoothing parameter (2018)
  17. Chen, Tianlei; Du, Pang: Mixture cure rate models with accelerated failures and nonparametric form of covariate effects (2018)
  18. Cui, Wenquan; Cheng, Haoyang; Sun, Jiajing: An RKHS-based approach to double-penalized regression in high-dimensional partially linear models (2018)
  19. Hwang, Sangheum; Jeong, Myong K.: Robust relevance vector machine for classification with variational inference (2018)
  20. John Monaco; Malka Gorfine; Li Hsu: General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv (2018) not zbMATH

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