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 290 articles , 1 standard article )

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  1. Devriendt, Sander; Antonio, Katrien; Reynkens, Tom; Verbelen, Roel: Sparse regression with multi-type regularized feature modeling (2021)
  2. Brentnall, Adam R.; Cuzick, Jack: Risk models for breast cancer and their validation (2020)
  3. Caro, Eduardo; Juan, Jesús; Cara, Javier: Periodically correlated models for short-term electricity load forecasting (2020)
  4. Lee, Gee Y.; Manski, Scott; Maiti, Tapabrata: Actuarial applications of word embedding models (2020)
  5. Lin, X. Sheldon; Yang, Shuai: Efficient dynamic hedging for large variable annuity portfolios with multiple underlying assets (2020)
  6. Li, Zheyuan; Wood, Simon N.: Faster model matrix crossproducts for large generalized linear models with discretized covariates (2020)
  7. Marra, Giampiero; Radice, Rosalba: Copula link-based additive models for right-censored event time data (2020)
  8. Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
  9. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  10. Murakami, Daisuke; Griffith, Daniel A.: A memory-free spatial additive mixed modeling for big spatial data (2020)
  11. Otneim, Håkon; Jullum, Martin; Tjøstheim, Dag: Pairwise local Fisher and naive Bayes: improving two standard discriminants (2020)
  12. Puth, Marie-Therese; Tutz, Gerhard; Heim, Nils; Münster, Eva; Schmid, Matthias; Berger, Moritz: Tree-based modeling of time-varying coefficients in discrete time-to-event models (2020)
  13. Razen, Alexander; Lang, Stefan: Random scaling factors in Bayesian distributional regression models with an application to real estate data (2020)
  14. Reiss, Philip T.; Xu, Meng: Tensor product splines and functional principal components (2020)
  15. Schomaker, Michael; Heumann, Christian: When and when not to use optimal model averaging (2020)
  16. Spiegel, Elmar; Kneib, Thomas; Otto-Sobotka, Fabian: Spatio-temporal expectile regression models (2020)
  17. Sun, Jinhui; Du, Pang; Miao, Hongyu; Liang, Hua: Robust feature screening procedures for single and mixed types of data (2020)
  18. Wang, Haixu; Cao, Jiguo: Estimating time-varying directed neural networks (2020)
  19. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  20. Wood, Simon N.: Rejoinder on: “Inference and computation with generalized additive models and their extensions” (2020)

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