| Preface | p. xi |
| A guide to notation | p. xiv |
| Model selection: data examples and introduction | p. 1 |
| Introduction | p. 1 |
| Egyptian skull development | p. 3 |
| Who wrote 'The Quiet Don'? | p. 7 |
| Survival data on primary biliary cirrhosis | p. 10 |
| Low birthweight data | p. 13 |
| Football match prediction | p. 15 |
| Speedskating | p. 17 |
| Preview of the following chapters | p. 19 |
| Notes on the literature | p. 20 |
| Akaike's information criterion | p. 22 |
| Information criteria for balancing fit with complexity | p. 22 |
| Maximum likelihood and the Kullback-Leibler distance | p. 23 |
| AIC and the Kullback-Leibler distance | p. 28 |
| Examples and illustrations | p. 32 |
| Takeuchi's model-robust information criterion | p. 43 |
| Corrected AIC for linear regression and autoregressive time series | p. 44 |
| AIC, corrected AIC and bootstrap-AIC for generalised linear models* | p. 46 |
| Behaviour of AIC for moderately misspecified models* | p. 49 |
| Cross-validation | p. 51 |
| Outlier-robust methods | p. 55 |
| Notes on the literature | p. 64 |
| Exercises | p. 66 |
| The Bayesian information criterion | p. 70 |
| Examples and illustrations of the BIC | p. 70 |
| Derivation of the BIC | p. 78 |
| Who wrote 'The Quiet Don'? | p. 82 |
| The BIC and AIC for hazard regression models | p. 85 |
| The deviance information criterion | p. 90 |
| Minimum description length | p. 94 |
| Notes on the literature | p. 96 |
| Exercises | p. 97 |
| A comparison of some selection methods | p. 99 |
| Comparing selectors: consistency, efficiency and parsimony | p. 99 |
| Prototype example: choosing between two normal models | p. 102 |
| Strong consistency and the Hannan-Quinn criterion | p. 106 |
| Mallow's C[subscript p] and its outlier-robust versions | p. 107 |
| Efficiency of a criterion | p. 108 |
| Efficient order selection in an autoregressive process and the FPE | p. 110 |
| Efficient selection of regression variables | p. 111 |
| Rates of convergence* | p. 112 |
| Taking the best of both worlds?* | p. 113 |
| Notes on the literature | p. 114 |
| Exercises | p. 115 |
| Bigger is not always better | p. 117 |
| Some concrete examples | p. 117 |
| Large-sample framework for the problem | p. 119 |
| A precise tolerance limit | p. 124 |
| Tolerance regions around parametric models | p. 126 |
| Computing tolerance thresholds and radii | p. 128 |
| How the 5000-m time influences the 10,000-m time | p. 130 |
| Large-sample calculus for AIC | p. 137 |
| Notes on the literature | p. 140 |
| Exercises | p. 140 |
| The focussed information criterion | p. 145 |
| Estimators and notation in submodels | p. 145 |
| The focussed information criterion, FIC | p. 146 |
| Limit distributions and mean squared errors in submodels | p. 148 |
| A bias-modified FIC | p. 150 |
| Calculation of the FIC | p. 153 |
| Illustrations and applications | p. 154 |
| Exact mean squared error calculations for linear regression* | p. 172 |
| The FIC for Cox proportional hazard regression models | p. 174 |
| Average-FIC | p. 179 |
| A Bayesian focussed information criterion* | p. 183 |
| Notes on the literature | p. 188 |
| Exercises | p. 189 |
| Frequentist and Bayesian model averaging | p. 192 |
| Estimators-post-selection | p. 192 |
| Smooth AIC, smooth BIC and smooth FIC weights | p. 193 |
| Distribution of model average estimators | p. 195 |
| What goes wrong when we ignore model selection? | p. 199 |
| Better confidence intervals | p. 206 |
| Shrinkage, ridge estimation and thresholding | p. 211 |
| Bayesian model averaging | p. 216 |
| A frequentist view of Bayesian model averaging* | p. 220 |
| Bayesian model selection with canonical normal priors* | p. 223 |
| Notes on the literature | p. 224 |
| Exercises | p. 225 |
| Lack-of-fit and goodness-of-fit tests | p. 227 |
| The principle of order selection | p. 227 |
| Asymptotic distribution of the order selection test | p. 229 |
| The probability of overfitting* | p. 232 |
| Score-based tests | p. 236 |
| Two or more covariates | p. 238 |
| Neyman's smooth tests and generalisations | p. 240 |
| A comparison between AIC and the BIC for model testing* | p. 242 |
| Goodness-of-fit monitoring processes for regression models* | p. 243 |
| Notes on the literature | p. 245 |
| Exercises | p. 246 |
| Model selection and averaging schemes in action | p. 248 |
| AIC and BIC selection for Egyptian skull development data | p. 248 |
| Low birthweight data: FIC plots and FIC selection per stratum | p. 252 |
| Survival data on PBC: FIC plots and FIC selection | p. 256 |
| Speedskating data: averaging over covariance structure models | p. 259 |
| Exercises | p. 266 |
| Further topics | p. 269 |
| Model selection in mixed models | p. 269 |
| Boundary parameters | p. 273 |
| Finite-sample corrections* | p. 281 |
| Model selection with missing data | p. 282 |
| When p and q grow with n | p. 284 |
| Notes on the literature | p. 285 |
| Overview of data examples | p. 287 |
| References | p. 293 |
| Author index | p. 306 |
| Subject index | p. 310 |
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