Incluye referencias bibliográficas (páginas 554-565) e índices.

1. Introducing R ; 1.1 Statistical packages and statistical modelling ; 1.2 Getting started in R ; 1.3 Reading data into R ; 1.4 Assignment and data generation ; 1.5 Displaying data ; 1.6 Data structures and the workspace ; 1.7 Transformations and data modification ; 1.8 Functions and suffixing ; 1.9 Graphical facilities ; 1.10 Text functions ; 1.11 Writing your own functions ; 1.12 Sorting and tabulation ; 1.13 Editing R code ; 1.14 Installing and using packages – 2. Statistical modelling and inference ; 2.1 Statistical models ; 2.2 Types of variables ; 2.3 Population models ; 2.4 Random sampling ; 2.5 The likelihood function ; 2.6 Inference for single parameter models ; 2.7 Inference with nuisance parameters ; 2.8 The effect of the sample design on inference ; 2.9 The exponential family -- 3 Regression and analysis of variance ; 3.1 An example ; 3.2 Strategies for model simplification ; 3.3 Stratified, weighted and clustered samples ; 3.4 Model criticism ; 3.5 The Box-Cox transformation family ; 3.6 Modelling and background information ; 3.7 Link functions and transformations ; 3.8 Regression models for prediction ; 3.9 Model choice and mean square prediction error ; 3.10 Model selection through cross-validation ; 3.11 Reduction of complex regression models ; 3.12 Sensitivity of the Box-Cox transformation ; 3.13 The use of regression models for calibration ; 3.14 Measurement error in the explanatory variables ; 3.15 Factorial designs ; 3.16 Unbalanced cross-classifications ; 3.17 Missing data ; 3.18 Approximate methods for missing data ; 3.19 Modelling of variance heterogeneity – 4. Binary response data ; 4.1 Binary responses ; 4.2 Transformations and link functions ; 4.2. Profile likelihoods for functions of parameters ; 4.3 Model criticism ; 4.4 Binary data with continuous covariates ; 4.5 Contingency table construction from binary data ; 4.6 The prediction of binary outcomes ; 4.7 Profile and conditional likelihoods in 2 2 tables ; 4.8 Three-dimensional contingency tables with a binary response ; 4.9 Multidimensional contingency tables with a binary response – 5. Multinomial and Poisson response data ; 5.1 The Poisson distribution ; 5.2 Cross-classified counts ; 5.3 Multicategory responses ; 5.4 Multinomial logit model ; 5.5 The Poisson-multinomial relation ; 5.6 Fitting the multinomial logit model ; 5.7 Ordered response categories ; 5.8 An Example ; 5.9 Structured multinomial responses – 6. Survival data ; 6.1 Introduction ; 6.2 The exponential distribution ; 6.3 Fitting the exponential distribution ; 6.4 Model criticism ; 6.5 Comparison with the normal family ; 6.6 Censoring ; 6.7 Likelihood function for censored observations ; 6.8 Probability plotting with censored data: the Kaplan-Meier estimator ; 6.9 The gamma distribution ; 6.10 The Weibull distribution ; 6.11 Maximum likelihood fitting of the Weibull distribution ; 6.12 The extreme value distribution ; 6.13 The reversed extreme value distribution ; 6.14 Survivor function plotting for the Weibull and extreme value distributions ; 6.15 The Cox proportional hazards model and the piecewise exponential distribution ; 6.16 Maximum likelihood fitting of the piecewise exponential distribution ; 6.17 Examples ; 6.18 The logistic and log-logistic distributions ; 6.19 The normal and lognormal distributions ; 6.20 Evaluating the proportional hazard assumption ; 6.21 Competing risks ; 6.22 Time-dependent explanatory variables ; 6.23 Discrete time models -- 7 Finite mixture models ; 7.1 Introduction 4337.2 Example - girl birthweights ; 7.3 Finite mixtures of distributions ; 7.4 Maximum likelihood in finite mixtures ; 7.5 Standard errors ; 7.6 Testing for the number of components ; 7.7 Likelihood 'spikes' ; 7.8 Galaxy data ; 7.9 Kernel density estimates – 8. Random effect models ; 8.1 Overdispersion ; 8.1.1 Testing for overdispersion ; 8.2 Conjugate random effects ; 8.3 Normal random effects ; 8.3.1 Predicting from the normal random effect model ; 8.4 Gaussian quadrature examples ; 8.5 Other specified random effect distributions ; 8.6 Arbitrary random effects ; 8.7 Examples ; 8.8 Random coefficient regression models ; 8.8.1 Example - the fabric fault data ; 8.9 Algorithms for mixture fitting ; 8.10 Modelling the mixing probabilities ; 8.11 Mixtures of mixtures – 9. Variance component models ; 9.1 Models with shared random effects ; 9.2 The normal/normal model ; 9.3 Exponential family two-level models ; 9.4 Other approaches ; 9.5 NPML estimation of the masses and mass-points ; 9.6 Random coefficient models ; 9.7 Variance component model fitting ; 9.8 Autoregressive random effect models ; 9.9 Latent variable models ; 9.10 IRT models ; 9.11 Spatial dependence ; 9.12 Multivariate correlated responses ; 9.13 Discreteness of the NPML estimate.