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 242 articles )

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  1. Hui, Francis K. C.; You, C.; Shang, H. L.; Müller, Samuel: Semiparametric regression using variational approximations (2019)
  2. Kneib, Thomas; Klein, Nadja; Lang, Stefan; Umlauf, Nikolaus: Modular regression -- a Lego system for building structured additive distributional regression models with tensor product interactions (2019)
  3. Lee, Wonyul; Miranda, Michelle F.; Rausch, Philip; Baladandayuthapani, Veerabhadran; Fazio, Massimo; Downs, J. Crawford; Morris, Jeffrey S.: Bayesian semiparametric functional mixed models for serially correlated functional data, with application to glaucoma data (2019)
  4. Liang, Kun: Empirical Bayes analysis of RNA sequencing experiments with auxiliary information (2019)
  5. Maeng, Hyeyoung; Fryzlewicz, Piotr: Regularised forecasting via smooth-rough partitioning of the regression coefficients (2019)
  6. Manghi, Roberto F.; Cysneiros, Francisco José A.; Paula, Gilberto A.: Generalized additive partial linear models for analyzing correlated data (2019)
  7. Mastrantonio, Gianluca; Lasinio, Giovanna Jona; Pollice, Alessio; Capotorti, Giulia; Teodonio, Lorenzo; Genova, Giulio; Blasi, Carlo: A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles (2019)
  8. Matsumoto, Takuji; Yamada, Yuji: Cross hedging using prediction error weather derivatives for loss of solar output prediction errors in electricity market (2019)
  9. Mhalla, Linda; Opitz, Thomas; Chavez-Demoulin, Valérie: Exceedance-based nonlinear regression of tail dependence (2019)
  10. Park, S. Y.; Li, C.; Mendoza Benavides, S. M.; van Heugten, E.; Staicu, A. M.: Conditional analysis for mixed covariates, with application to feed intake of lactating sows (2019)
  11. Pechon, Florian; Denuit, Michel; Trufin, Julien: Multivariate modelling of multiple guarantees in motor insurance of a household (2019)
  12. Rodríguez-Álvarez, María Xosé; Durban, Maria; Lee, Dae-Jin; Eilers, Paul H. C.: On the estimation of variance parameters in non-standard generalised linear mixed models: application to penalised smoothing (2019)
  13. Sadhanala, Veeranjaneyulu; Tibshirani, Ryan J.: Additive models with trend filtering (2019)
  14. Spiegel, Elmar; Kneib, Thomas; Otto-Sobotka, Fabian: Generalized additive models with flexible response functions (2019)
  15. Thaden, Hauke; Klein, Nadja; Kneib, Thomas: Multivariate effect priors in bivariate semiparametric recursive Gaussian models (2019)
  16. Tsokos, Alkeos; Narayanan, Santhosh; Kosmidis, Ioannis; Baio, Gianluca; Cucuringu, Mihai; Whitaker, Gavin; Király, Franz: Modeling outcomes of soccer matches (2019)
  17. Yoshida, Takuma; Naito, Kanta: Regression with stagewise minimization on risk function (2019)
  18. Youngman, Benjamin D.: Generalized additive models for exceedances of high thresholds with an application to return level estimation for U.S. wind gusts (2019)
  19. Chatla, Suneel Babu; Shmueli, Galit: Efficient estimation of COM-Poisson regression and a generalized additive model (2018)
  20. Denuit, Michel; Legrand, Catherine: Risk classification in life and health insurance: extension to continuous covariates (2018)

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