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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 |
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