Bootstrapping spectra: Methods, comparisons and application to knock data

Abstract

The problem of confidence interval estimation for spectra is addressed. Unlike asymptotic techniques, bootstrap techniques provide accurate measures of confidence in that they maintain the preset level, in particular for small sample sizes and non-Gaussian data. We investigate some recently proposed methods, a time domain bootstrap approach, the so-called tapered block bootstrap and a combined time domain and frequency domain approach, the so-called autoregressive-aided periodogram bootstrap. We compare the methods with a well-established frequency domain residual based bootstrap technique in view of confidence accuracy. Confidence interval estimates for spectra of knock data are shown.