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 198 articles )

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  1. Amini, Morteza; Roozbeh, Mahdi: Improving the prediction performance of the Lasso by subtracting the additive structural noises (2019)
  2. Arnone, Eleonora; Azzimonti, Laura; Nobile, Fabio; Sangalli, Laura M.: Modeling spatially dependent functional data via regression with differential regularization (2019)
  3. Cao, Jiguo; Soiaporn, Kunlaya; Carroll, Raymond J.; Ruppert, David: Modeling and prediction of multiple correlated functional outcomes (2019)
  4. Djeundje, Viani Biatat; Crook, Jonathan: Dynamic survival models with varying coefficients for credit risks. (2019)
  5. Djeundje, Viani Biatat; Crook, Jonathan: Identifying hidden patterns in credit risk survival data using generalised additive models (2019)
  6. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)
  7. Manghi, Roberto F.; Cysneiros, Francisco José A.; Paula, Gilberto A.: Generalized additive partial linear models for analyzing correlated data (2019)
  8. Tsokos, Alkeos; Narayanan, Santhosh; Kosmidis, Ioannis; Baio, Gianluca; Cucuringu, Mihai; Whitaker, Gavin; Király, Franz: Modeling outcomes of soccer matches (2019)
  9. Yoshida, Takuma; Naito, Kanta: Regression with stagewise minimization on risk function (2019)
  10. Chatla, Suneel Babu; Shmueli, Galit: Efficient estimation of COM-Poisson regression and a generalized additive model (2018)
  11. Denuit, Michel; Legrand, Catherine: Risk classification in life and health insurance: extension to continuous covariates (2018)
  12. Gao, Guangyuan; Meng, Shengwang: Stochastic claims reserving via a Bayesian spline model with random loss ratio effects (2018)
  13. 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)
  14. Henckaerts, Roel; Antonio, Katrien; Clijsters, Maxime; Verbelen, Roel: A data driven binning strategy for the construction of insurance tariff classes (2018)
  15. Li, Weiping; Chen, Su: The early exercise premium in American options by using nonparametric regressions (2018)
  16. Marchetti, Yuliya; Nguyen, Hai; Braverman, Amy; Cressie, Noel: Spatial data compression via adaptive dispersion clustering (2018)
  17. Papathomas, Michail: On the correspondence from Bayesian log-linear modelling to logistic regression modelling with (g)-priors (2018)
  18. Pazira, Hassan; Augugliaro, Luigi; Wit, Ernst: Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter (2018)
  19. Pitt, David; Li, Jackie; Lim, Tian Kang: Smoothing Poisson common factor model for projecting mortality jointly for both sexes (2018)
  20. Rothenhäusler, Dominik; Ernest, Jan; Bühlmann, Peter: Causal inference in partially linear structural equation models (2018)

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