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 258 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. Sun, Jinhui; Du, Pang; Miao, Hongyu; Liang, Hua: Robust feature screening procedures for single and mixed types of data (2020)
  6. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  7. Wang, Jian-Yong; Yu, Han: The measure on the original space from a product measure (2020)
  8. Wang, Lu; Xue, Lan; Yang, Lijian: Estimation of additive frontier functions with shape constraints (2020)
  9. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  10. Zhao, Shi; Bakoyannis, Giorgos; Lourens, Spencer; Tu, Wanzhu: Comparison of nonlinear curves and surfaces (2020)
  11. Francom, Devin; Sansó, Bruno; Bulaevskaya, Vera; Lucas, Donald; Simpson, Matthew: Inferring atmospheric release characteristics in a large computer experiment using Bayesian adaptive splines (2019)
  12. 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)
  13. Guo, Zheng-Chu; Shi, Lei: Fast and strong convergence of online learning algorithms (2019)
  14. Huang, Whitney K.; Cooley, Daniel S.; Ebert-Uphoff, Imme; Chen, Chen; Chatterjee, Snigdhansu: New exploratory tools for extremal dependence: (\chi) networks and annual extremal networks (2019)
  15. Kim, Choongrak; Yang, Hojin: Diagnostic measures for kernel ridge regression on reproducing kernel Hilbert space (2019)
  16. Kneib, Thomas; Klein, Nadja; Lang, Stefan; Umlauf, Nikolaus: Modular regression -- a Lego system for building structured additive distributional regression models with tensor product interactions (2019)
  17. Shi, Jian; Liu, Anna; Wang, Yuedong: Spline density estimation and inference with model-based penalties (2019)
  18. Stasinopoulos, M. D.; Rigby, R. A.; Heller, G. Z.; De Bastiani, F.: Comments on “Modular regression -- a Lego system for building structured additive distributional regression models with tensor product interactions” (2019)
  19. Tan, Zhiqiang; Zhang, Cun-Hui: Doubly penalized estimation in additive regression with high-dimensional data (2019)
  20. Tuo, Rui: Adjustments to computer models via projected kernel calibration (2019)

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