Foundations of Linear and Generalized Linear Models

Content: Preface xi    1 Introduction to Linear and Generalized Linear Models 1    1.1 Components of a Generalized Linear Model 2    1.2 Quantitative/Qualitative Explanatory Variables and Interpreting Effects 6    1.3 Model Matrices and Model Vector Spaces 10    1.4 Identifiability and Estimability 13    1.5 Example: Using Software to Fit a GLM 15    Chapter Notes 20    Exercises 21    2 Linear Models: Least Squares Theory 26    2.1 Least Squares Model Fitting 27    2.2 Projections of Data Onto Model Spaces 33    2.3 Linear Model Examples: Projections and SS Decompositions 41    2.4 Summarizing Variability in a Linear Model 49    2.5 Residuals Leverage and Influence 56    2.6 Example: Summarizing the Fit of a Linear Model 62    2.7 Optimality of Least Squares and Generalized Least Squares 67    Chapter Notes 71    Exercises 71    3 Normal Linear Models: Statistical Inference 80    3.1 Distribution Theory for Normal Variates 81    3.2 Significance Tests for Normal Linear Models 86    3.3 Confidence Intervals and Prediction Intervals for Normal Linear Models 95    3.4 Example: Normal Linear Model Inference 99    3.5 Multiple Comparisons: Bonferroni Tukey and FDR Methods 107    Chapter Notes 111    Exercises 112    4 Generalized Linear Models: Model Fitting and Inference 120    4.1 Exponential Dispersion Family Distributions for a GLM 120    4.2 Likelihood and Asymptotic Distributions for GLMs 123    4.3 Likelihood-Ratio/Wald/Score Methods of Inference for GLM Parameters 128    4.4 Deviance of a GLM Model Comparison and Model Checking 132    4.5 Fitting Generalized Linear Models 138    4.6 Selecting Explanatory Variables for a GLM 143    4.7 Example: Building a GLM 149    Appendix: GLM Analogs of Orthogonality Results for Linear Models 156    Chapter Notes 158    Exercises 159    5 Models for Binary Data 165    5.1 Link Functions for Binary Data 165    5.2 Logistic Regression: Properties and Interpretations 168    5.3 Inference About Parameters of Logistic Regression Models 172    5.4 Logistic Regression Model Fitting 176    5.5 Deviance and Goodness of Fit for Binary GLMs 179    5.6 Probit and Complementary Log   Log Models 183    5.7 Examples: Binary Data Modeling 186    Chapter Notes 193    Exercises 194    6 Multinomial Response Models 202    6.1 Nominal Responses: Baseline-Category Logit Models 203    6.2 Ordinal Responses: Cumulative Logit and Probit Models 209    6.3 Examples: Nominal and Ordinal Responses 216    Chapter Notes 223    Exercises 223    7 Models for Count Data 228    7.1 Poisson GLMs for Counts and Rates 229    7.2 Poisson/Multinomial Models for Contingency Tables 235    7.3 Negative Binomial GLMS 247    7.4 Models for Zero-Inflated Data 250    7.5 Example: Modeling Count Data 254    Chapter Notes 259    Exercises 260    8 Quasi-Likelihood Methods 268    8.1 Variance Inflation for Overdispersed Poisson and Binomial GLMs 269    8.2 Beta-Binomial Models and Quasi-Likelihood Alternatives 272    8.3 Quasi-Likelihood and Model Misspecification 278    Chapter Notes 282    Exercises 282    9 Modeling Correlated Responses 286    9.1 Marginal Models and Models with Random Effects 287    9.2 Normal Linear Mixed Models 294    9.3 Fitting and Prediction for Normal Linear Mixed Models 302    9.4 Binomial and Poisson GLMMs 307    9.5 GLMM Fitting Inference and Prediction 311    9.6 Marginal Modeling and Generalized Estimating Equations 314    9.7 Example: Modeling Correlated Survey Responses 319    Chapter Notes 322    Exercises 324    10 Bayesian Linear and Generalized Linear Modeling 333    10.1 The Bayesian Approach to Statistical Inference 333    10.2 Bayesian Linear Models 340    10.3 Bayesian Generalized Linear Models 347    10.4 Empirical Bayes and Hierarchical Bayes Modeling 351    Chapter Notes 357    Exercises 359    11 Extensions of Generalized Linear Models 364    11.1 Robust Regression and Regularization Methods for Fitting Models 365    11.2 Modeling With Large p 375    11.3 Smoothing Generalized Additive Models and Other GLM Extensions 378    Chapter Notes 386    Exercises 388    Appendix A Supplemental Data Analysis Exercises 391    Appendix B Solution Outlines for Selected Exercises 396    References 410    Author Index 427    Example Index 433    Subject Index 435