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 301 articles , 1 standard article )

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  1. Aeberhard, William H.; Cantoni, Eva; Marra, Giampiero; Radice, Rosalba: Robust fitting for generalized additive models for location, scale and shape (2021)
  2. Castro-Camilo, Daniela; Mhalla, Linda; Opitz, Thomas: Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures (2021)
  3. Correia, Hannah E.; Abebe, Asheber: Regularised rank quasi-likelihood estimation for generalised additive models (2021)
  4. Devriendt, Sander; Antonio, Katrien; Reynkens, Tom; Verbelen, Roel: Sparse regression with multi-type regularized feature modeling (2021)
  5. Dodd, Erengul; Forster, Jonathan J.; Bijak, Jakub; Smith, Peter W. F.: Stochastic modelling and projection of mortality improvements using a hybrid parametric/semi-parametric age-period-cohort model (2021)
  6. Gressani, Oswaldo; Lambert, Philippe: Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines (2021)
  7. Kalogridis, Ioannis; Van Aelst, Stefan: (M)-type penalized splines with auxiliary scale estimation (2021)
  8. Liu, Yan; Lu, Minggen; Mcmahan, Christopher S.: A penalized likelihood approach for efficiently estimating a partially linear additive transformation model with current status data (2021)
  9. Machado, Robson J. M.; van den Hout, Ardo; Marra, Giampiero: Penalised maximum likelihood estimation in multi-state models for interval-censored data (2021)
  10. Marra, Giampiero; Farcomeni, Alessio; Radice, Rosalba: Link-based survival additive models under mixed censoring to assess risks of hospital-acquired infections (2021)
  11. Arku, Dennis; Doku-Amponsah, Kwabena; Howard, Nathaniel K.: A Markov-modulated tree-based gradient boosting model for auto-insurance risk premium pricing (2020)
  12. Brentnall, Adam R.; Cuzick, Jack: Risk models for breast cancer and their validation (2020)
  13. Caro, Eduardo; Juan, Jesús; Cara, Javier: Periodically correlated models for short-term electricity load forecasting (2020)
  14. Lee, Gee Y.; Manski, Scott; Maiti, Tapabrata: Actuarial applications of word embedding models (2020)
  15. Lin, X. Sheldon; Yang, Shuai: Efficient dynamic hedging for large variable annuity portfolios with multiple underlying assets (2020)
  16. Li, Zheyuan; Wood, Simon N.: Faster model matrix crossproducts for large generalized linear models with discretized covariates (2020)
  17. Marra, Giampiero; Radice, Rosalba: Copula link-based additive models for right-censored event time data (2020)
  18. Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
  19. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  20. Murakami, Daisuke; Griffith, Daniel A.: A memory-free spatial additive mixed modeling for big spatial data (2020)

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