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. Caro, Eduardo; Juan, Jesús; Cara, Javier: Periodically correlated models for short-term electricity load forecasting (2020)
  2. Lee, Gee Y.; Manski, Scott; Maiti, Tapabrata: Actuarial applications of word embedding models (2020)
  3. Li, Zheyuan; Wood, Simon N.: Faster model matrix crossproducts for large generalized linear models with discretized covariates (2020)
  4. Otneim, Håkon; Jullum, Martin; Tjøstheim, Dag: Pairwise local Fisher and naive Bayes: improving two standard discriminants (2020)
  5. Reiss, Philip T.; Xu, Meng: Tensor product splines and functional principal components (2020)
  6. Sun, Jinhui; Du, Pang; Miao, Hongyu; Liang, Hua: Robust feature screening procedures for single and mixed types of data (2020)
  7. Zhang, Xiaoke; Zhong, Qixian; Wang, Jane-Ling: A new approach to varying-coefficient additive models with longitudinal covariates (2020)
  8. Amini, Morteza; Roozbeh, Mahdi: Improving the prediction performance of the Lasso by subtracting the additive structural noises (2019)
  9. Arnone, Eleonora; Azzimonti, Laura; Nobile, Fabio; Sangalli, Laura M.: Modeling spatially dependent functional data via regression with differential regularization (2019)
  10. Brizzi, Francesco; Birrell, Paul J.; Plummer, Martyn T.; Kirwan, Peter; Brown, Alison E.; Delpech, Valerie C.; Gill, O. Noel; De Angelis, Daniela: Extending Bayesian back-calculation to estimate age and time specific HIV incidence (2019)
  11. Cao, Jiguo; Soiaporn, Kunlaya; Carroll, Raymond J.; Ruppert, David: Modeling and prediction of multiple correlated functional outcomes (2019)
  12. Djeundje, Viani Biatat; Crook, Jonathan: Identifying hidden patterns in credit risk survival data using generalised additive models (2019)
  13. Djeundje, Viani Biatat; Crook, Jonathan: Dynamic survival models with varying coefficients for credit risks. (2019)
  14. Dziak, John J.; Coffman, Donna L.; Reimherr, Matthew; Petrovich, Justin; Li, Runze; Shiffman, Saul; Shiyko, Mariya P.: Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: interpretability for applied scientists (2019)
  15. Engebretsen, Solveig; Glad, Ingrid K.: Additive monotone regression in high and lower dimensions (2019)
  16. Filippou, Panagiota; Kneib, Thomas; Marra, Giampiero; Radice, Rosalba: A trivariate additive regression model with arbitrary link functions and varying correlation matrix (2019)
  17. Gao, Guangyuan; Meng, Shengwang; Wüthrich, Mario V.: Claims frequency modeling using telematics car driving data (2019)
  18. Gao, Guangyuan; Wüthrich, Mario V.; Yang, Hanfang: Evaluation of driving risk at different speeds (2019)
  19. Ghahari, Azar; Newlands, Nathaniel K.; Lyubchich, Vyacheslav; Gel, Yulia R.: Deep learning at the interface of agricultural insurance risk and spatio-temporal uncertainty in weather extremes (2019)
  20. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)

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