TABLE OF CONTENTS This appendix contains some of the MATLAB [ ] functions that were purposely written for this book.
| bootmf.m | Bootstrap matched filter |
| bootrsp.m | Bootstrap resampling procedure (univariate) |
| bootrsp2.m | Bootstrap resampling procedure (bivariate) |
| boottest.m | Bootstrap hypothesis test (pivoted) |
| boottestnp.m | Bootstrap hypothesis test (unpivoted) |
| boottestvs.m | Bootstrap hypothesis test (variance stabilisation) |
| bpestcir.m | Block bootstrap variance estimation |
| bpestdb.m | Double block bootstrap variance estimation |
| bspecest.m | Bootstrapping spectral density (residual method) |
| bspecest2.m | Bootstrapping spectral density (double block) |
| confint.m | Confidence interval estimator (percentile- t) |
| confintp.m | Confidence interval estimator (bootstrap percentile) |
| confinth.m | Confidence interval estimator (bootstrap hybrid) |
| jackest.m | Jackknife estimator |
| jackrsp.m | Jackknife resampling procedure |
| segmcirc.m | Segment extraction in circular block bootstrap |
| segments.m | Segment extraction in block of blocks bootstrap |
| smooth.m | A running line smoother |
function [d] = bootmf(x,s,far,B,B1)% d = bootmf(x,s,far);%% Inputs:%% x - observations under the model X(t)=s(t)+Z(t)% where Z(t) is correlated interference and% s(t) is a known signal;% can be a matrix with each set of obs. in a column% s - known signal [default=ones(size(x,1),1)]% far - detector s false...
