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 138 articles , 2 standard articles )

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  1. Bruno, Francesca; Greco, Fedele; Ventrucci, Massimo: Non-parametric regression on compositional covariates using Bayesian P-splines (2016)
  2. Helwig, Nathaniel E.: Efficient estimation of variance components in nonparametric mixed-effects models with large samples (2016)
  3. Lukas, Mark A.; de Hoog, Frank R.; Anderssen, Robert S.: Practical use of robust GCV and modified GCV for spline smoothing (2016)
  4. Radice, Rosalba; Marra, Giampiero; Wojtyś, Małgorzata: Copula regression spline models for binary outcomes (2016)
  5. Zaidi, Nayyar A.; Webb, Geoffrey I.; Carman, Mark J.; Petitjean, François; Cerquides, Jesús: $\textALR^n$: accelerated higher-order logistic regression (2016)
  6. Zhang, Chong; Liu, Yufeng; Wu, Yichao: On quantile regression in reproducing kernel Hilbert spaces with the data sparsity constraint (2016)
  7. Bahraoui, Zuhair; Bolancé, Catalina; Pelican, Elena; Vernic, Raluca: On the bivariate Sarmanov distribution and copula. an application on insurance data using truncated marginal distributions (2015)
  8. Casals, Martí; Langohr, Klaus; Carrasco, Josep Lluís; Rönnegård, Lars: Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles: a simulation study (2015)
  9. Cheng, Guang; Zhang, Hao Helen; Shang, Zuofeng: Sparse and efficient estimation for partial spline models with increasing dimension (2015)
  10. Eilers, Paul H.C.; Marx, Brian D.; Durbán, Maria: Twenty years of P-splines (invited article) (2015)
  11. Hahn, Eugene D.; López Martín, María del Mar: Robust project management with the tilted beta distribution (2015)
  12. Martínez-Flórez, Guillermo; Bolfarine, Heleno; Gómez, Héctor W.: Likelihood-based inference for the power regression model (2015)
  13. Martín-Fernández, Josep-Antoni; Daunis-i-Estadella, Josep; Mateu-Figueras, Glòria: On the interpretation of differences between groups for compositional data (2015)
  14. Molina, David; Rueda, Maria del Mar; Arcos, Antonio; Ranalli, Maria Giovanna: Multinomial logistic estimation in dual frame surveys (2015)
  15. Pogány, Tibor K.; Nadarajah, Saralees: A note on “Double bounded Kumaraswamy-power series class of distributions” (2015)
  16. Rady, E.A.; Kilany, N.M.; Eliwa, S.A.: Estimation in mixed-effects functional ANOVA models (2015)
  17. Rodríguez-Álvarez, María Xosé; Lee, Dae-Jin; Kneib, Thomas; Durbán, María; Eilers, Paul: Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm (2015)
  18. Tan, Matthias Hwai Yong: Sequential Bayesian polynomial chaos model selection for estimation of sensitivity indices (2015)
  19. Arribas-Gil, Ana; Bertin, Karine; Meza, Cristian; Rivoirard, Vincent: Lasso-type estimators for semiparametric nonlinear mixed-effects models estimation (2014)
  20. Chervoneva, Inna; Freydin, Boris; Hipszer, Brian; Apanasovich, Tatiyana V.; Joseph, Jeffrey I.: Estimation of nonlinear differential equation model for glucose-insulin dynamics in type I diabetic patients using generalized smoothing (2014)

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