Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models

Introduction Binary Response Heart Disease Example Logistic Regression Inference Diagnostics Model Selection Goodness of Fit Estimation Problems Binomial and Proportion Responses Binomial Regression Model Inference Pearson's Ï 2 Statistic Overdispersion Quasi-Binomial Beta Regression Variations on Logistic Regression Latent Variables Link Functions Prospective and Retrospective Sampling Prediction and Effective Doses Matched Case-Control Studies Count Regression Poisson Regression Dispersed Poisson Model Rate Models Negative Binomial Zero Inflated Count Models Contingency Tables Two-by-Two Tables Larger Two-Way Tables Correspondence Analysis Matched Pairs Three-Way Contingency Tables Ordinal Variables Multinomial Data Multinomial Logit Model Linear Discriminant Analysis Hierarchical or Nested Responses Ordinal Multinomial Responses Generalized Linear Models GLM Definition Fitting a GLM Hypothesis Tests GLM Diagnostics Sandwich Estimation Robust Estimation Other GLMs Gamma GLM Inverse Gaussian GLM Joint Modeling of the Mean and Dispersion Quasi-Likelihood GLM Tweedie GLM Random Effects Estimation Inference Estimating Random Effects Prediction Diagnostics Blocks as Random Effects Split Plots Nested Effects Crossed Effects Multilevel Models Repeated Measures and Longitudinal Data Longitudinal DataRepeated MeasuresMultiple Response Multilevel Models Bayesian Mixed Effect Models STAN INLA Discussion Mixed Effect Models for Nonnormal Responses Generalized Linear Mixed Models Inference Binary Response Count Response Generalized Estimating Equations Nonparametric Regression Kernel Estimators Splines Local Polynomials Confidence Bands Wavelets Discussion of Methods Multivariate Predictors Additive Models Modeling Ozone Concentration 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 Random Forests Classification Trees Classification Using Forests Neural Networks Statistical Models as NNs Feed-Forward Neural Network with One Hidden Layer NN Application Conclusion Appendix A: Likelihood Theory Appendix B: About R Bibliography Index