Cover -- Half-title -- Title -- Copyright -- Contents -- Preface -- Notations -- General notation -- Latin symbols -- Greek symbols -- Acronyms -- 1 Introduction -- 2 The bootstrap principle -- 2.1 The principle of resampling -- 2.1.1 Some theoretical results for the mean -- 2.1.3 The parametric bootstrap -- 2.1.4 Bootstrap resampling for dependent data -- 2.2 The principle of pivoting and variance stabilisation -- 2.2.1 Some examples -- 2.3 Limitations of the bootstrap -- 2.4 Trends in bootstrap resampling -- 2.5 Summary -- 3 Signal detection with the bootstrap -- 3.1 Principles of hypothesis testing -- 3.1.1 Sub-optimal detection -- 3.2 Hypothesis testing with the bootstrap -- 3.3 The role of pivoting -- 3.4 Variance estimation -- 3.5 Detection through regression -- 3.6 The bootstrap matched filter -- 3.6.1 Tolerance interval bootstrap matched filter -- 3.7 Summary -- 4 Bootstrap model selection -- 4.1 Preliminaries -- 4.2 Model selection -- 4.3 Model selection in linear models -- 4.3.1 Model selection based on prediction -- 4.3.2 Bootstrap based model selection -- 4.3.3 A consistent bootstrap method -- 4.3.4 Dependent data in linear models -- 4.4 Model selection in nonlinear models -- 4.4.1 Data model -- 4.4.2 Use of bootstrap in model selection -- 4.5 Order selection in autoregressions -- 4.6 Detection of sources using bootstrap techniques -- 4.6.1 Bootstrap based detection -- 4.6.2 Null distribution estimation -- 4.6.3 Bias correction -- 4.6.4 Simulations -- 4.7 Summary -- 5 Real data bootstrap applications -- 5.1 Optimal sensor placement for knock detection -- 5.1.1 Motivation -- 5.1.2 Data model -- 5.1.3 Bootstrap tests -- 5.1.4 The experiment -- 5.2 Confidence intervals for aircraft parameters -- 5.2.1 Introduction -- 5.2.2 Results with real passive acoustic data -- 5.3 Landmine detection.

5.4 Noise floor estimation in over-the-horizon radar -- 5.4.1 the Principle of trimmed mean -- 5.4.2 Optimal trimming -- 5.4.3 Noise floor estimation -- 5.5 Model order selection for corneal elevation -- 5.6 Summary -- Appendix 1 Matlab codes for the examples -- A1.1 Basic non-parametric bootstrap estimation -- A1.2 The parametric bootstrap -- A1.3 Bootstrap resampling for dependent data -- A1.4 The principle of pivoting and variance stabilisation -- A1.5 Limitations of bootstrap procedure -- A1.6 Hypothesis testing -- A1.7 The bootstrap matched filter -- A1.8 Bootstrap model selection -- A1.9 Noise floor estimation -- Appendix 2 Bootstrap Matlab Toolbox -- A2.1 Bootstrap Toolbox Contents -- References -- Index.