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. Paciorek, Christopher J.; Yanosky, Jeff D.; Puett, Robin C.; Laden, Francine; Suh, Helen H.: Practical large-scale spatio-temporal modeling of particulate matter concentrations (2009)
  2. Ruppert, David; Wand, M. P.; Carroll, Raymond J.: Semiparametric regression during 2003--2007 (2009)
  3. Schnabel, Sabine K.; Eilers, Paul H. C.: Optimal expectile smoothing (2009)
  4. Welham, S. J.; Thompson, R.: A note on bimodality in the log-likelihood function for penalized spline mixed models (2009)
  5. Yan, Xin; Su, Xiao Gang: Linear regression analysis. Theory and computing. (2009)
  6. Belitz, Christiane; Lang, Stefan: Simultaneous selection of variables and smoothing parameters in structured additive regression models (2008)
  7. Berk, Richard A.: Statistical learning from a regression perspective (2008)
  8. Bilancia, Massimo; Stea, Girolamo: Timescale effect estimation in time-series studies of air pollution and health: a singular spectrum analysis approach (2008)
  9. Binder, Harald; Sauerbrei, Willi: Increasing the usefulness of additive spline models by knot removal (2008)
  10. Höhle, Michael; Paul, Michaela: Count data regression charts for the monitoring of surveillance time series (2008)
  11. López-de-Ullibarri, Ignacio; Cao, Ricardo; Cadarso-Suárez, Carmen; Lado, María J.: Nonparametric estimation of conditional ROC curves: application to discrimination tasks in computerized detection of early breast cancer (2008)
  12. Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G.; Lado, María J.: Assessing continuous bivariate effects among different groups through nonparametric regression models: an application to breast cancer detection (2008)
  13. Scheipl, Fabian; Greven, Sonja; Küchenhoff, Helmut: Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models (2008)
  14. Schmid, Matthias; Hothorn, Torsten: Boosting additive models using component-wise P-splines (2008)
  15. Wood, Simon N.: Fast stable direct fitting and smoothness selection for generalized additive models (2008)
  16. Wüthrich, Mario V.; Merz, Michael: Stochastic claims reserving methods in insurance (2008)
  17. Yamada, Yuji: Optimal hedging of prediction errors using prediction errors (2008)
  18. Augustin, Nicole H.; Lang, Stefan; Musio, Monica; Von Wilpert, Klaus: A spatial model for the needle losses of pine-trees in the forests of Baden-Württemberg: an application of Bayesian structured additive regression (2007)
  19. Leitenstorfer, Florian; Tutz, Gerhard: Knot selection by boosting techniques (2007)
  20. Paciorek, Christopher J.: Computational techniques for spatial logistic regression with large data sets (2007)

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