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 273 articles , 3 standard articles )

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  1. Antonelli, Joseph; Mazumdar, Maitreyi; Bellinger, David; Christiani, David; Wright, Robert; Coull, Brent: Estimating the health effects of environmental mixtures using Bayesian semiparametric regression and sparsity inducing priors (2020)
  2. Bartel, Felix; Hielscher, Ralf; Potts, Daniel: Fast cross-validation in harmonic approximation (2020)
  3. Chen, Chen; Liao, Qifeng: ANOVA Gaussian process modeling for high-dimensional stochastic computational models (2020)
  4. Gao, Zhenguo; Du, Pang; Jin, Ran; Robertson, John L.: Surface temperature monitoring in liver procurement via functional variance change-point analysis (2020)
  5. Kounchev, O.; Render, H.; Tsachev, T.: On a class of (L)-splines of order 4: fast algorithms for interpolation and smoothing (2020)
  6. Lamboni, Matieyendou: Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices (2020)
  7. Li, Ting; Zhu, Zhongyi: Inference for generalized partial functional linear regression (2020)
  8. Liu, Meimei; Shang, Zuofeng; Cheng, Guang: Nonparametric distributed learning under general designs (2020)
  9. Spiegel, Elmar; Kneib, Thomas; Otto-Sobotka, Fabian: Spatio-temporal expectile regression models (2020)
  10. Sung, Chih-Li; Wang, Wenjia; Plumlee, Matthew; Haaland, Benjamin: Multiresolution functional ANOVA for large-scale, many-input computer experiments (2020)
  11. Sun, Jinhui; Du, Pang; Miao, Hongyu; Liang, Hua: Robust feature screening procedures for single and mixed types of data (2020)
  12. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  13. Wang, Jian-Yong; Yu, Han: The measure on the original space from a product measure (2020)
  14. Wang, Lu; Xue, Lan; Yang, Lijian: Estimation of additive frontier functions with shape constraints (2020)
  15. Wei, Yuting; Fang, Billy; Wainwright, Martin J.: From Gauss to Kolmogorov: localized measures of complexity for ellipses (2020)
  16. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  17. Xing, Xin; Liu, Meimei; Ma, Ping; Zhong, Wenxuan: Minimax nonparametric parallelism test (2020)
  18. Zhao, Shi; Bakoyannis, Giorgos; Lourens, Spencer; Tu, Wanzhu: Comparison of nonlinear curves and surfaces (2020)
  19. Francom, Devin; Sansó, Bruno; Bulaevskaya, Vera; Lucas, Donald; Simpson, Matthew: Inferring atmospheric release characteristics in a large computer experiment using Bayesian adaptive splines (2019)
  20. 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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