What is included with this book?
Lurdes Yoshiko Tani Inoue is a Brazilian-born statistician of Japanese descent, who specializes in Bayesian inference. She works as a professor of biostatistics in the University of Washington School of Public Health.
Preface | p. xiii |
Acknowledgments | p. xvii |
Introduction | p. 1 |
Controversies | p. 1 |
A guided tour of decision theory | p. 6 |
Foundations | p. 11 |
Coherence | p. 13 |
The "Dutch Book" theorem | p. 15 |
Betting odds | p. 15 |
Coherence and the axioms of probability | p. 17 |
Coherent conditional probabilities | p. 20 |
The implications of Dutch Book theorems | p. 21 |
Temporal coherence | p. 24 |
Scoring rules and the axioms of probabilities | p. 26 |
Exercises | p. 27 |
Utility | p. 33 |
St. Petersburg paradox | p. 34 |
Expected utility theory and the theory of means | p. 37 |
Utility and means | p. 37 |
Associative means | p. 38 |
Functional means | p. 39 |
The expected utility principle | p. 40 |
The von Neumann-Morgenstern representation theorem | p. 42 |
Axioms | p. 42 |
Representation of preferences via expected utility | p. 44 |
Allais' criticism | p. 48 |
Extensions | p. 50 |
Exercises | p. 50 |
Utility in action | p. 55 |
The "standard gamble" | p. 56 |
Utility of money | p. 57 |
Certainty equivalents | p. 57 |
Risk aversion | p. 57 |
A measure of risk aversion | p. 60 |
Utility functions for medical decisions | p. 63 |
Length and quality of life | p. 63 |
Standard gamble for health states | p. 64 |
The time trade-off methods | p. 64 |
Relation between QALYs and utilities | p. 65 |
Utilities for time in ill health | p. 66 |
Difficulties in assessing utility | p. 69 |
Exercises | p. 70 |
Ramsey and Savage | p. 75 |
Ramsey's theory | p. 76 |
Savage's theory | p. 81 |
Notation and overview | p. 81 |
The sure thing principle | p. 82 |
Conditional and a posteriori preferences | p. 85 |
Subjective probability | p. 85 |
Utility and expected utility | p. 90 |
Allais revisited | p. 91 |
Ellsberg paradox | p. 92 |
Exercises | p. 93 |
State independence | p. 97 |
Horse lotteries | p. 98 |
State-dependent utilities | p. 100 |
State-independent utilities | p. 101 |
Anscombe-Aumann representation theorem | p. 103 |
Exercises | p. 105 |
Statistical Decision Theory | p. 109 |
Decision functions | p. 111 |
Basic concepts | p. 112 |
The loss function | p. 112 |
Minimax | p. 114 |
Expected utility principle | p. 116 |
Illustrations | p. 117 |
Data-based decisions | p. 120 |
Risk | p. 120 |
Optimality principles | p. 121 |
Rationality principles and the Likelihood Principle | p. 123 |
Nuisance parameters | p. 125 |
The travel insurance example | p. 126 |
Randomized decision rules | p. 131 |
Classification and hypothesis tests | p. 133 |
Hypothesis testing | p. 133 |
Multiple hypothesis testing | p. 136 |
Classification | p. 139 |
Estimation | p. 140 |
Point estimation | p. 140 |
Interval inference | p. 143 |
Minimax-Bayes connection | p. 144 |
Exercises | p. 150 |
Admissibility | p. 155 |
Admissibility and completeness | p. 156 |
Admissibility and minimax | p. 158 |
Admissibility and Bayes | p. 159 |
Proper Bayes rules | p. 159 |
Generalized Bayes rules | p. 160 |
Complete classes | p. 164 |
Completeness and Bayes | p. 164 |
Sufficiency and the Rao-Blackwell inequality | p. 165 |
The Neyman-Pearson lemma | p. 167 |
Using the same ¿ level across studies with different sample sizes is inadmissible | p. 168 |
Exercises | p. 171 |
Shrinkage | p. 175 |
The Stein effect | p. 176 |
Geometric and empirical Bayes heuristics | p. 179 |
Is x too big for $$? | p. 179 |
Empirical Bayes shrinkage | p. 181 |
General shrinkage functions | p. 183 |
Unbiased estimation of the risk of x+g(x) | p. 183 |
Bayes and minimax shrinkage | p. 185 |
Shrinkage with different likelihood and losses | p. 188 |
Exercises | p. 188 |
Scoring rules | p. 191 |
Betting and forecasting | p. 192 |
Scoring rules | p. 193 |
Definition | p. 193 |
Proper scoring rules | p. 194 |
The quadratic scoring rules | p. 195 |
Scoring rules that are not proper | p. 196 |
Local scoring rules | p. 197 |
Calibration and refinement | p. 200 |
The well-calibrated forecaster | p. 200 |
Are Bayesians well calibrated? | p. 205 |
Exercises | p. 207 |
Choosing models | p. 209 |
The "true model" perspective | p. 210 |
Model probabilities | p. 210 |
Model selection and Bayes factors | p. 212 |
Model averaging for prediction and selection | p. 213 |
Model elaborations | p. 216 |
Exercises | p. 219 |
Optimal Design | p. 221 |
Dynamic programming | p. 223 |
History | p. 224 |
The travel insurance example revisited | p. 226 |
Dynamic programming | p. 230 |
Two-stage finite decision problems | p. 230 |
More than two stages | p. 233 |
Trading off immediate gains and information | p. 235 |
The secretary problem | p. 235 |
The prophet inequality | p. 239 |
Sequential clinical trials | p. 241 |
Two-armed bandit problems | p. 241 |
Adaptive designs for binary outcomes | p. 242 |
Variable selection in multiple regression | p. 245 |
Computing | p. 248 |
Exercises | p. 251 |
Changes in utility as information | p. 255 |
Measuring the value of information | p. 256 |
The value function | p. 256 |
Information from a perfect experiment | p. 258 |
Information from a statistical experiment | p. 259 |
The distribution of information | p. 264 |
Examples | p. 265 |
Tasting grapes | p. 265 |
Medical testing | p. 266 |
Hypothesis testing | p. 273 |
Lindley information | p. 276 |
Definition | p. 276 |
Properties | p. 278 |
Computing | p. 280 |
Optimal design | p. 281 |
Minimax and the value of information | p. 283 |
Exercises | p. 285 |
Sample size | p. 289 |
Decision-theoretic approaches to sample size | p. 290 |
Sample size and power | p. 290 |
Sample size as a decision problem | p. 290 |
Bayes and minimax optimal sample size | p. 292 |
A minimax paradox | p. 293 |
Goal sampling | p. 295 |
Computing | p. 298 |
Examples | p. 302 |
Point estimation with quadratic loss | p. 302 |
Composite hypothesis testing | p. 304 |
A two-action problem with linear utility | p. 306 |
Lindley information for exponential data | p. 309 |
Multicenter clinical trials | p. 311 |
Exercises | p. 316 |
Stopping | p. 323 |
Historical note | p. 324 |
A motivating example | p. 326 |
Bayesian optimal stopping | p. 328 |
Notation | p. 328 |
Bayes sequential procedure | p. 329 |
Bayes truncated procedure | p. 330 |
Examples | p. 332 |
Hypotheses testing | p. 332 |
An example with equivalence between sequential and fixed sample size designs | p. 336 |
Sequential sampling to reduce uncertainty | p. 337 |
The stopping rule principle | p. 339 |
Stopping rules and the Likelihood Principle | p. 339 |
Sampling to a foregone conclusion | p. 340 |
Exercises | p. 342 |
p. 345 | |
Notation | p. 345 |
Relations | p. 349 |
Probability (density) functions of some distributions | p. 350 |
Conjugate updating | p. 350 |
References | p. 353 |
Index | p. 367 |
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