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.
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. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/
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.
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Extending the Linear Model with R: Generalized Linear, Mixed Effects and ...
Julian J. Faraway
No preview available - 2016
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Page iii - The Analysis of Time Series — An Introduction, Fifth Edition C. Chatfield Applied Bayesian Forecasting and Time Series Analysis A. Pole, M. West, and J. Harrison Applied Nonparametric Statistical Methods, Third Edition P. Sprent and NC Smeeton Applied Statistics — Principles and Examples DR Cox and EJ Snell Bayesian Data Analysis A. Gelman, J. Carlin, H.
Page iii - Analysis C. Chatfield and AJ Collins Introduction to Optimization Methods and their Applications in Statistics BS Everitt Large Sample Methods in Statistics PK Sen and J. da Motta Singer Markov Chain Monte Carlo — Stochastic Simulation for Bayesian Inference D.
Page iv - Statistical Analysis of Reliability Data MJ Crowder, AC Kimber, TJ Sweeting, and RL Smith Statistical Methods for SPC and TQM D. Bissell Statistical Methods in Agriculture and Experimental Biology, Second Edition R. Mead, RN Curnow, and AM Hasted Statistical Process Control — Theory and Practice, Third Edition GB Wetherill and DW Brown Statistical Theory, Fourth Edition BW Lindgren Statistics for Accountants S. Letchford Statistics for Epidemiology Nicholas P.Jewell Statistics for Technology •...
Page 292 - Some Infinity Theory for Predictor Ensembles." Technical Report 522, Department of Statistics, University of California, Berkeley, California. Breiman, L. (2001a) "Random Forests." Machine Learning 45: 5-32. Breiman, L. (200 Ib) "Statistical Modeling: Two Cultures," (with discussion) Statistical Science 16: 199-231. Cleveland, W. (1979) "Robust Locally Weighted Regression and Smoothing Scatterplots.