Offers an outstanding practical survey of statistical methods extended from the regression model
Presents all of the linear model extensions using a common framework, making estimation, inference, and model building and checking clearly understandable
Includes an introductory chapter that reviews the linear model and the basics of using R
Provides a companion Web site featuring all of the datasets used in the book.

Table of ContentsINTRODUCTION

BINOMIAL DATA
Challenger Disaster Example
Binomial Regression Model
Inference
Tolerance Distribution
Interpreting Odds
Prospective and Retrospective Sampling
Choice of Link Function
Estimation Problems
Goodness of Fit
Prediction and Effective Doses
Overdispersion
Matched Case-Control Studies

COUNT REGRESSION
Poisson Regression
Rate Models
Negative Binomial

CONTINGENCY TABLES
Two-by-Two Tables
Larger Two-Way Tables
Matched Pairs
Three-Way Contingency Tables
Ordinal Variables

MULTINOMIAL DATA
Multinomial Logit Model
Hierarchical or Nested Responses
Ordinal Multinomial Responses

GENERALIZED LINEAR MODELS
GLM Definition
Fitting a GLM
Hypothesis Tests
GLM Diagnostics

OTHER GLMS
Gamma GLM
Inverse Gaussian GLM
Joint Modeling of the Mean and Dispersion
Quasi-Likelihood

RANDOM EFFECTS
Estimation
Inference
Predicting Random Effects
Blocks as Random Effects
Split Plots
Nested Effects
Crossed Effects
Multilevel Models

REPEATED MEASURES AND LONGITUDINAL DATA
Longitudinal Data
Repeated Measures
Multiple Response Multilevel Models

MIXED EFFECT MODELS FOR NONNORMAL RESPONSES
Generalized Linear Mixed Models
Generalized Estimating Equations

NONPARAMETRIC REGRESSION
Kernel Estimators
Splines
Local Polynomials
Wavelets
Other Methods
Comparison of Methods
Multivariate Predictors

ADDITIVE MODELS
Additive Models Using the gam Package
Additive Models Using mgcv
Generalized Additive Models
Alternating Conditional Expectations
Additivity and Variance Stabilization
Generalized Additive Mixed Models
Multivariate Adaptive Regression Splines

TREES
Regression Trees
Tree Pruning
Classification Trees

NEURAL NETWORKS
Statistical Models as NNs
Feed-Forward Neural Network with One Hidden Layer
NN Application
Conclusion

APPENDICES
Likelihood Theory
R Information
Bibliography
Index