| Preface | p. ix |
| Notations | p. xii |
| Introduction | p. 1 |
| The bootstrap principle | p. 11 |
| The principle of resampling | p. 11 |
| Some theoretical results for the mean | p. 17 |
| Examples of non-parametric bootstrap estimation | p. 19 |
| The parametric bootstrap | p. 26 |
| Bootstrap resampling for dependent data | p. 28 |
| Examples of dependent data bootstrap estimation | p. 33 |
| The principle of pivoting and variance stabilisation | p. 49 |
| Some examples | p. 51 |
| Limitations of the bootstrap | p. 57 |
| Trends in bootstrap resampling | p. 59 |
| Summary | p. 60 |
| Signal detection with the bootstrap | p. 62 |
| Principles of hypothesis testing | p. 62 |
| Sub-optimal detection | p. 72 |
| Hypothesis testing with the bootstrap | p. 73 |
| The role of pivoting | p. 74 |
| Variance estimation | p. 78 |
| Detection through regression | p. 83 |
| The bootstrap matched filter | p. 93 |
| Tolerance interval bootstrap matched filter | p. 99 |
| Summary | p. 101 |
| Bootstrap model selection | p. 103 |
| Preliminaries | p. 103 |
| Model selection | p. 105 |
| Model selection in linear models | p. 106 |
| Model selection based on prediction | p. 107 |
| Bootstrap based model selection | p. 108 |
| A consistent bootstrap method | p. 109 |
| Dependent data in linear models | p. 114 |
| Model selection in nonlinear models | p. 114 |
| Data model | p. 114 |
| Use of bootstrap in model selection | p. 115 |
| Order selection in autoregressions | p. 117 |
| Detection of sources using bootstrap techniques | p. 119 |
| Bootstrap based detection | p. 121 |
| Null distribution estimation | p. 124 |
| Bias correction | p. 126 |
| Simulations | p. 127 |
| Summary | p. 127 |
| Real data bootstrap applications | p. 130 |
| Optimal sensor placement for knock detection | p. 130 |
| Motivation | p. 131 |
| Data model | p. 131 |
| Bootstrap tests | p. 134 |
| The experiment | p. 135 |
| Confidence intervals for aircraft parameters | p. 136 |
| Introduction | p. 136 |
| Results with real passive acoustic data | p. 139 |
| Landmine detection | p. 143 |
| Noise floor estimation in over-the-horizon radar | p. 147 |
| Principle of the trimmed mean | p. 148 |
| Optimal trimming | p. 150 |
| Noise floor estimation | p. 151 |
| Model order selection for corneal elevation | p. 154 |
| Summary | p. 158 |
| Matlab codes for the examples | p. 159 |
| Basic non-parametric bootstrap estimation | p. 159 |
| The parametric bootstrap | p. 160 |
| Bootstrap resampling for dependent data | p. 160 |
| The principle of pivoting and variance stabilisation | p. 161 |
| Limitations of bootstrap procedure | p. 163 |
| Hypothesis testing | p. 163 |
| The bootstrap matched filter | p. 167 |
| Bootstrap model selection | p. 167 |
| Noise floor estimation | p. 170 |
| Bootstrap Matlab Toolbox | p. 174 |
| References | p. 201 |
| Index | p. 215 |
| Table of Contents provided by Ingram. All Rights Reserved. |
