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 200 articles , 3 standard articles )

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  1. Borgonovo, Emanuele; Buzzard, Gregery T.; Wendell, Richard E.: A global tolerance approach to sensitivity analysis in linear programming (2018)
  2. Borgonovo, Emanuele; Morris, Max D.; Plischke, Elmar: Functional ANOVA with multiple distributions: implications for the sensitivity analysis of computer experiments (2018)
  3. Chen, Chun-Shu; Huang, Yi-Tsz: Some characteristics on the selection of spline smoothing parameter (2018)
  4. Chen, Tianlei; Du, Pang: Mixture cure rate models with accelerated failures and nonparametric form of covariate effects (2018)
  5. Hwang, Sangheum; Jeong, Myong K.: Robust relevance vector machine for classification with variational inference (2018)
  6. Lamboni, Matieyendou: Global sensitivity analysis: a generalized, unbiased and optimal estimator of total-effect variance (2018)
  7. Lin, Junhong; Rosasco, Lorenzo: Generalization properties of doubly stochastic learning algorithms (2018)
  8. Zhang, Chong; Pham, Minh; Fu, Sheng; Liu, Yufeng: Robust multicategory support vector machines using difference convex algorithm (2018)
  9. Dieuleveut, Aymeric; Flammarion, Nicolas; Bach, Francis: Harder, better, faster, stronger convergence rates for least-squares regression (2017)
  10. Griebel, Michael; Oswald, Peter: Stable splittings of Hilbert spaces of functions of infinitely many variables (2017)
  11. John V. Monaco, Malka Gorfine, Li Hsu: General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv (2017) arXiv
  12. Karagiannis, Georgios; Lin, Guang: On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models (2017)
  13. Sánchez-González, Mariola; Durbán, María; Lee, Dae-Jin; Cañellas, Isabel; Sixto, Hortensia: Smooth additive mixed models for predicting aboveground biomass (2017)
  14. Şerbănescu, Cristina; Pop, Cosmina-Elena: Data analysis and statistical estimation for time series: improving presentation and interpretation (2017)
  15. Wood, Simon N.: Generalized additive models. An introduction with R. (2017)
  16. Bruno, Francesca; Greco, Fedele; Ventrucci, Massimo: Non-parametric regression on compositional covariates using Bayesian P-splines (2016)
  17. Chun, Hyonho; Lee, Myung Hee; Fleet, James C.; Oh, Ji Hwan: Graphical models via joint quantile regression with component selection (2016)
  18. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  19. Helwig, Nathaniel E.: Efficient estimation of variance components in nonparametric mixed-effects models with large samples (2016)
  20. Kong, Dehan; Maity, Arnab; Hsu, Fang-Chi; Tzeng, Jung-Ying: Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine (2016)

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