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. Gao, Guangyuan; Meng, Shengwang: Stochastic claims reserving via a Bayesian spline model with random loss ratio effects (2018)
  2. Gladish, Daniel W.; Pagendam, Daniel E.; Peeters, Luk J. M.; Kuhnert, Petra M.; Vaze, Jai: Emulation engines: choice and quantification of uncertainty for complex hydrological models (2018)
  3. Henckaerts, Roel; Antonio, Katrien; Clijsters, Maxime; Verbelen, Roel: A data driven binning strategy for the construction of insurance tariff classes (2018)
  4. Huggins, Richard; Stoklosa, Jakub; Roach, Cameron; Yip, Paul: Estimating the size of an open population using sparse capture-recapture data (2018)
  5. Li, Weiping; Chen, Su: The early exercise premium in American options by using nonparametric regressions (2018)
  6. Marchetti, Yuliya; Nguyen, Hai; Braverman, Amy; Cressie, Noel: Spatial data compression via adaptive dispersion clustering (2018)
  7. Mosammam, Ali M.; Mateu, Jorge: A penalized likelihood method for nonseparable space-time generalized additive models (2018)
  8. Papathomas, Michail: On the correspondence from Bayesian log-linear modelling to logistic regression modelling with (g)-priors (2018)
  9. Pazira, Hassan; Augugliaro, Luigi; Wit, Ernst: Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter (2018)
  10. Pitt, David; Li, Jackie; Lim, Tian Kang: Smoothing Poisson common factor model for projecting mortality jointly for both sexes (2018)
  11. Pütz, Peter; Kneib, Thomas: A penalized spline estimator for fixed effects panel data models (2018)
  12. Rothenhäusler, Dominik; Ernest, Jan; Bühlmann, Peter: Causal inference in partially linear structural equation models (2018)
  13. Segal, Brian D.; Elliott, Michael R.; Braun, Thomas; Jiang, Hui: P-splines with an (\ell_1) penalty for repeated measures (2018)
  14. Sharma, Kshitij; Chavez-Demoulin, Valérie; Dillenbourg, Pierre: Nonstationary modelling of tail dependence of two subjects’ concentration (2018)
  15. Sun, Peng; Kim, Inyoung; Lee, Ki-Ahm: Dual-semiparametric regression using weighted Dirichlet process mixture (2018)
  16. Tutz, Gerhard; Berger, Moritz: Tree-structured modelling of categorical predictors in generalized additive regression (2018)
  17. Wang, Xin; Roy, Vivekananda; Zhu, Zhengyuan: A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach (2018)
  18. Wojtyś, Małgorzata; Marra, Giampiero; Radice, Rosalba: Copula based generalized additive models for location, scale and shape with non-random sample selection (2018)
  19. Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
  20. Zhang, Nanxi; Huang, Alan: Profile likelihood ratio tests for parameter inferences in generalised single-index models (2018)

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