An Introduction to Bootstrap Methods with Applications to R

SUBJECT INDEX

632 estimator, 2, 8, 25, 36–41, 43, 68, 136

A posteriori probabilities, 33, 171

A priori probability, 34

ABC (approximate bootstrap confidence) intervals, 79, 85, 87, 88, 90–94, 96, 97

acceleration constant, 85–87, 91

AIC (Akaike's information crterion), 12, 149–152

Alternating conditional expectation (ACE), 65

historical notes, 69

Apparent error rate bias

bootstrap estimation, 2, 36, 37

discrimination, 32, 35, 36, 43, 67

error rate estimation, 8, 25, 35, 36, 37 67

Area under the concentration vs. time curve (AUC), 177, 178

Asymptotic theory, 5, 6, 7, 9, 25, 61, 67, 80, 83–85, 87, 88, 90, 95, 96, 104, 119, 147, 190, 192–198, 200

asymptotic consistency, 2, 3, 5, 25, 140 164, 182

asymptotic normality, 3, 54, 192

central limit theory, 102

inconsistency, 164, 191–198, 200

Autoregressive bootstrap (ARB), 118, 119, 120, 123, 197, 198

Bayes' theorem, 33

Bayesian bootstrap, 137, 138, 141, 143

BIC (Bayesian information criterion), 149, 154, 182

Binary Dose Response Model, 108

Bioequivalence, 9, 52, 162–165, 183, 188, 189

Efron's patch data example, 51

individual bioequivalence, 9

population bioequivalence, 165, 183

Block bootstrap, 60, 118–120, 123–127, 129, 130

circular block bootstrap (CBB), 124, 129, 130

fixed block bootstrap, 125

history, 123

moving block bootstrap, 3, 120, 123, 126, 127, 129, 130, 198

non-overlapping block bootstrap (NBB), 124

spatial data

asymptotics, 147

Kriging, 144–146, 182

stationary block bootstrap (SBB), 124, 125

tapered block bootstrap (TBB), ...