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Author

Faraway, Julian James.

Title Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models / Julian J. Faraway, University of Bath, UK.

Published	Boca Raton CRC Press, Taylor & Francis Group, [2016]
	©2016.

Copies

Location	Call No.	Status
UniM ERC	519.538 FARA	DUE 13-10-23

Edition	Second edition.
Physical description	xiii, 399 pages : illustrations ; 24 cm.
Series	Chapman & Hall/CRC texts in statistical science series.
	Texts in statistical science.
Notes	"A Chapman & Hall book."
Bibliography	Includes bibliographical references (pages 385-394) and index.
Contents	Machine generated contents note: 1.Introduction -- 2.Binary Response -- 2.1.Heart Disease Example -- 2.2.Logistic Regression -- 2.3.Inference -- 2.4.Diagnostics -- 2.5.Model Selection -- 2.6.Goodness of Fit -- 2.7.Estimation Problems -- 3.Binomial and Proportion Responses -- 3.1.Binomial Regression Model -- 3.2.Inference -- 3.3.Pearson's Χ2 Statistic -- 3.4.Overdispersion -- 3.5.Quasi-Binomial -- 3.6.Beta Regression -- 4.Variations on Logistic Regression -- 4.1.Latent Variables -- 4.2.Link Functions -- 4.3.Prospective and Retrospective Sampling -- 4.4.Prediction and Effective Doses -- 4.5.Matched Case-Control Studies -- 5.Count Regression -- 5.1.Poisson Regression -- 5.2.Dispersed Poisson Model -- 5.3.Rate Models -- 5.4.Negative Binomial -- 5.5.Zero Inflated Count Models -- 6.Contingency Tables -- 6.1.Two-by-Two Tables -- 6.2.Larger Two-Way Tables -- 6.3.Correspondence Analysis -- 6.4.Matched Pairs -- 6.5.Three-Way Contingency Tables -- 6.6.Ordinal Variables --
	Contents note continued: 7.Multinomial Data -- 7.1.Multinomial Logit Model -- 7.2.Linear Discriminant Analysis -- 7.3.Hierarchical or Nested Responses -- 7.4.Ordinal Multinomial Responses -- 8.Generalized Linear Models -- 8.1.GLM Definition -- 8.2.Fitting a GLM -- 8.3.Hypothesis Tests -- 8.4.GLM Diagnostics -- 8.5.Sandwich Estimation -- 8.6.Robust Estimation -- 9.Other GLMs -- 9.1.Gamma GLM -- 9.2.Inverse Gaussian GLM -- 9.3.Joint Modeling of the Mean and Dispersion -- 9.4.Quasi-Likelihood GLM -- 9.5.Tweedie GLM -- 10.Random Effects -- 10.1.Estimation -- 10.2.Inference -- 10.3.Estimating Random Effects -- 10.4.Prediction -- 10.5.Diagnostics -- 10.6.Blocks as Random Effects -- 10.7.Split Plots -- 10.8.Nested Effects -- 10.9.Crossed Effects -- 10.10.Multilevel Models -- 11.Repeated Measures and Longitudinal Data -- 11.1.Longitudinal Data -- 11.2.Repeated Measures -- 11.3.Multiple Response Multilevel Models -- 12.Bayesian Mixed Effect Models -- 12.1.STAN -- 12.2.INLA --
	Contents note continued: 12.3.Discussion -- 13.Mixed Effect Models for Nonnormal Responses -- 13.1.Generalized Linear Mixed Models -- 13.2.Inference -- 13.3.Binary Response -- 13.4.Count Response -- 13.5.Generalized Estimating Equations -- 14.Nonparametric Regression -- 14.1.Kernel Estimators -- 14.2.Splines -- 14.3.Local Polynomials -- 14.4.Confidence Bands -- 14.5.Wavelets -- 14.6.Discussion of Methods -- 14.7.Multivariate Predictors -- 15.Additive Models -- 15.1.Modeling Ozone Concentration -- 15.2.Additive Models Using mgcv -- 15.3.Generalized Additive Models -- 15.4.Alternating Conditional Expectations -- 15.5.Additivity and Variance Stabilization -- 15.6.Generalized Additive Mixed Models -- 15.7.Multivariate Adaptive Regression Splines -- 16.Trees -- 16.1.Regression Trees -- 16.2.Tree Pruning -- 16.3.Random Forests -- 16.4.Classification Trees -- 16.5.Classification Using Forests -- 17.Neural Networks -- 17.1.Statistical Models as NNs --
	Contents note continued: 17.2.Feed-Forward Neural Network with One Hidden Layer -- 17.3.NN Application -- 17.4.Conclusion -- A.Likelihood Theory -- A.1.Maximum Likelihood -- A.2.Hypothesis Testing -- A.3.Model Selection -- B.About R.
Subject	Analysis of variance.
	Regression analysis.
	R (Computer program language) -- Mathematical models.
ISBN	9781498720960 (alkaline paper)
	149872096X (alkaline paper)

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