Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway’s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies.par Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author’s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site at www.stat.lsa.umich.edu/ faraway/ELM holds all of the data described in the book.par Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught. (Source: http://cran.r-project.org/web/packages)

References in zbMATH (referenced in 17 articles )

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  1. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  2. Boj, Eva; Caballé, Adrià; Delicado, Pedro; Esteve, Anna; Fortiana, Josep: Global and local distance-based generalized linear models (2016)
  3. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  4. Meneses, Antonio; Naya, Salvador; López-de-Ullibarri, Ignacio; Tarrío-Saavedra, Javier: Nonparametric method for estimating the distribution of time to failure of engineering materials (2016)
  5. Tattar, Prabhanjan N.; Ramaiah, Suresh; Manjunath, B. G.: A course in statistics with R (2016)
  6. Du, Xiao Fang; Leung, Stephen C.H.; Zhang, Jin Long; Lai, K.K.: Demand forecasting of perishable farm products using support vector machine (2013)
  7. Ardalan, A.; Sadooghi-Alvandi, S. M.; Nematollahi, A. R.: The two-piece normal-Laplace distribution (2012)
  8. Gross, Elizabeth; Drton, Mathias; Petrović, Sonja: Maximum likelihood degree of variance component models (2012)
  9. Klar, Bernhard; Meintanis, Simos G.: Specification tests for the response distribution in generalized linear models (2012)
  10. Kong, Maiying; Cambon, Alex; Smith, Michael J.: Extended logistic regression model for studies with interrupted events, seasonal trend, and serial correlation (2012)
  11. Samuh, Monjed H.; Grilli, Leonardo; Rampichini, Carla; Salmaso, Luigi; Lunardon, Nicola: The use of permutation tests for variance components in linear mixed models (2012)
  12. Anderes, Ethan B.; Stein, Michael L.: Local likelihood estimation for nonstationary random fields (2011)
  13. Denker, Manfred; Facca, Tina: Conformal measures and density estimation (2011)
  14. Zhang, Jin-Ting: Statistical inferences for linear models with functional responses (2011)
  15. Zhang, Tonglin; Lin, Ge: Spatial scan statistics in loglinear models (2009)
  16. Sun, Jiafeng; Frees, Edward W.; Rosenberg, Marjorie A.: Heavy-tailed longitudinal data modeling using copulas (2008)
  17. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2006)