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 90 articles )

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  1. Bevilacqua, Moreno; Alegria, Alfredo; Velandia, Daira; Porcu, Emilio: Composite likelihood inference for multivariate Gaussian random fields (2016)
  2. Boj, Eva; Caballé, Adrià; Delicado, Pedro; Esteve, Anna; Fortiana, Josep: Global and local distance-based generalized linear models (2016)
  3. Bruno, Francesca; Greco, Fedele; Ventrucci, Massimo: Non-parametric regression on compositional covariates using Bayesian P-splines (2016)
  4. Gagnon, Jacob; Liang, Hua; Liu, Anna: Spherical radial approximation for nested mixed effects models (2016)
  5. 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)
  6. Heinzl, Felix; Tutz, Gerhard: Additive mixed models with approximate Dirichlet process mixtures: the EM approach (2016)
  7. Hofner, Benjamin; Kneib, Thomas; Hothorn, Torsten: A unified framework of constrained regression (2016)
  8. Klein, Nadja; Kneib, Thomas: Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach (2016)
  9. Lim, Changwon: Interval-valued data regression using nonparametric additive models (2016)
  10. Barthelmé, Simon; Chopin, Nicolas: The Poisson transform for unnormalised statistical models (2015)
  11. Braun, W.John: Visualization of evidence in regression with the QR decomposition (2015)
  12. Ivanescu, Andrada E.; Staicu, Ana-Maria; Scheipl, Fabian; Greven, Sonja: Penalized function-on-function regression (2015)
  13. McLean, Mathew W.; Hooker, Giles; Ruppert, David: Restricted likelihood ratio tests for linearity in scalar-on-function regression (2015)
  14. Proença, Isabel; Faustino, Horácio C.: Modelling bilateral intra-industry trade indexes with panel data: a semiparametric approach (2015)
  15. Pya, Natalya; Wood, Simon N.: Shape constrained additive models (2015)
  16. 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)
  17. Sperlich, Stefan; Theler, Raoul: Modeling heterogeneity: a praise for varying-coefficient models in causal analysis (2015)
  18. Usset, Joseph; Maity, Arnab; Staicu, Ana-Maria; Schwartzman, Armin: Glacier terminus estimation from Landsat image intensity profiles (2015)
  19. Vanegas, Luis Hernando; Paula, Gilberto A.: A semiparametric approach for joint modeling of median and skewness (2015)
  20. Vatter, Thibault; Chavez-Demoulin, Valérie: Generalized additive models for conditional dependence structures (2015)

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