R package gamair: Data for ”GAMs: An Introduction with R”. Data sets and scripts used in the book ”Generalized Additive Models: An Introduction with R”, Wood (2006) CRC: The aim of this book is to present a comprehensive introduction to linear, generalized linear, generalized additive and mixed models. Moreover, the book contains explanations of the theory underlying the statistical methods and material on statistical modelling in R. The book is written to be accessible and the author used a fairly smooth way even in the case of advanced statistical notions. The book is intended as a text both for the students from the last two years of an undergraduate math/statistics programmme upwards and researchers. The prerequisite is an honest course in probability and statistics. Finally, let us note that the book includes some practical examples illustrating the theory and corresponding exercises. The appendix is devoted to some matrix algebra.

References in zbMATH (referenced in 103 articles )

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  1. Donat, Francesco; Marra, Giampiero: Semi-parametric bivariate polychotomous ordinal regression (2017)
  2. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  3. Langrock, Roland; Kneib, Thomas; Glennie, Richard; Michelot, Théo: Markov-switching generalized additive models (2017)
  4. Wood, Simon N.: Generalized additive models. An introduction with R. (2017)
  5. Yu, Yan; Wu, Chaojiang; Zhang, Yuankun: Penalised spline estimation for generalised partially linear single-index models (2017)
  6. Amato, Umberto; Antoniadis, Anestis; De Feis, Italia: Additive model selection (2016)
  7. Bevilacqua, Moreno; Alegria, Alfredo; Velandia, Daira; Porcu, Emilio: Composite likelihood inference for multivariate Gaussian random fields (2016)
  8. Boj, Eva; Caballé, Adrià; Delicado, Pedro; Esteve, Anna; Fortiana, Josep: Global and local distance-based generalized linear models (2016)
  9. Bruno, Francesca; Greco, Fedele; Ventrucci, Massimo: Non-parametric regression on compositional covariates using Bayesian P-splines (2016)
  10. Dutta, Subhajit; Sarkar, Soham; Ghosh, Anil K.: Multi-scale classification using localized spatial depth (2016)
  11. Gagnon, Jacob; Liang, Hua; Liu, Anna: Spherical radial approximation for nested mixed effects models (2016)
  12. Gray, Brian R.; Lyubchich, Vyacheslav; Gel, Yulia R.; Rogala, James T.; Robertson, Dale M.; Wei, Xiaoqiao: Estimation of river and stream temperature trends under haphazard sampling (2016)
  13. Heinzl, Felix; Tutz, Gerhard: Additive mixed models with approximate Dirichlet process mixtures: the EM approach (2016)
  14. Helwig, Nathaniel E.: Efficient estimation of variance components in nonparametric mixed-effects models with large samples (2016)
  15. Hofner, Benjamin; Kneib, Thomas; Hothorn, Torsten: A unified framework of constrained regression (2016)
  16. Klein, Nadja; Kneib, Thomas: Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach (2016)
  17. Lim, Changwon: Interval-valued data regression using nonparametric additive models (2016)
  18. Ma, Haiqiang; Zhu, Zhongyi: Continuously dynamic additive models for functional data (2016)
  19. Radice, Rosalba; Marra, Giampiero; Wojtyś, Małgorzata: Copula regression spline models for binary outcomes (2016)
  20. Relvas, Carlos Eduardo M.; Paula, Gilberto A.: Partially linear models with first-order autoregressive symmetric errors (2016)

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