gamair

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 317 articles , 1 standard article )

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  1. Kruse, René-Marcel; Silbersdorff, Alexander; Säfken, Benjamin: Model averaging for linear mixed models via augmented Lagrangian (2022)
  2. Rügamer, David; Baumann, Philipp F. M.; Greven, Sonja: Selective inference for additive and linear mixed models (2022)
  3. Aeberhard, William H.; Cantoni, Eva; Marra, Giampiero; Radice, Rosalba: Robust fitting for generalized additive models for location, scale and shape (2021)
  4. Berger, Moritz; Tutz, Gerhard: Transition models for count data: a flexible alternative to fixed distribution models (2021)
  5. 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)
  6. Correia, Hannah E.; Abebe, Asheber: Regularised rank quasi-likelihood estimation for generalised additive models (2021)
  7. Devriendt, Sander; Antonio, Katrien; Reynkens, Tom; Verbelen, Roel: Sparse regression with multi-type regularized feature modeling (2021)
  8. 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)
  9. Gressani, Oswaldo; Lambert, Philippe: Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines (2021)
  10. Henckaerts, Roel; Côté, Marie-Pier; Antonio, Katrien; Verbelen, Roel: Boosting insights in insurance tariff plans with tree-based machine learning methods (2021)
  11. He, Yaoyao; Fan, Huiling; Lei, Xiaohui; Wan, Jinhong: A runoff probability density prediction method based on B-spline quantile regression and kernel density estimation (2021)
  12. Hooker, Giles; Mentch, Lucas; Zhou, Siyu: Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance (2021)
  13. Jeon, Jeong Min; Park, Byeong U.; van Keilegom, Ingrid: Additive regression for non-Euclidean responses and predictors (2021)
  14. Kalogridis, Ioannis; Van Aelst, Stefan: (M)-type penalized splines with auxiliary scale estimation (2021)
  15. Kauermann, Göran; Ali, Mehboob: Semi-parametric regression when some (expensive) covariates are missing by design (2021)
  16. Klein, Nadja; Carlan, Manuel; Kneib, Thomas; Lang, Stefan; Wagner, Helga: Bayesian effect selection in structured additive distributional regression models (2021)
  17. Kowal, Daniel R.: Dynamic regression models for time-ordered functional data (2021)
  18. Lambert, Philippe: Fast Bayesian inference using Laplace approximations in nonparametric double additive location-scale models with right- and interval-censored data (2021)
  19. 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)
  20. Machado, Robson J. M.; van den Hout, Ardo; Marra, Giampiero: Penalised maximum likelihood estimation in multi-state models for interval-censored data (2021)

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